This document discusses using repeated simulations of a crisp neural network to obtain quasi-fuzzy weight sets (QFWS) that can be used to initialize fuzzy neural networks. The key points are:
1) A crisp neural network is repeatedly trained on input-output data to model an unknown function. The connection weights change with each simulation.
2) Recording the weights from multiple simulations produces quasi-fuzzy weight sets, where each weight is a fuzzy set rather than a single value.
3) These QFWS can provide initial solutions for training type-I fuzzy neural networks with reduced computational complexity compared to random initialization.
4) The QFWS follow fuzzy arithmetic and allow both numerical and linguistic data to
Functional brain parcellations break the brain into modules of regions with similar connectivity profiles. Parcellations exist over a range of scales from large scale networks to smaller specialized cortical areas. Larger parcels have higher homogeneity while stability is highest at the group level and more variable at the individual level. A number of metrics are used to evaluate parcellations including homogeneity, stability, and agreement with functional tasks. Individual parcellations provide more detail than group parcellations but estimating them jointly improves accuracy. Gradients provide an alternative to parcels by describing continuous connectivity patterns in the brain.
This document summarizes a study that compares fuzzy logic and neuro-fuzzy models for predicting direct current in motors. Fuzzy logic and neuro-fuzzy systems were used to model the relationship between motor torque, power, speed (inputs) and current (output). Both techniques were tested on a dataset of 507 samples. The neuro-fuzzy inference system (ANFIS) performed slightly better than the fuzzy logic system at predicting motor current, demonstrating the benefits of combining fuzzy logic with neural networks.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
A Learning Linguistic Teaching Control for a Multi-Area Electric Power SystemCSCJournals
This paper presents a new methodology for designing a neuro-fuzzy control for complex physical systems. By developing a Neural -Fuzzy system learning with linguistic teaching signals. The advantage of this technique is that, produce a simple and well-performing system because it selects the fuzzy sets and the numerical numbers and process both numerical and linguistic information. This approach is able to process and learn numerical information as well as linguistic information. The proposed control scheme is applied to a multi-area power system with hydraulic and thermal turbines.
One page summary of master thesis "Mathematical Analysis of Neural Networks"Alina Leidinger
This is a one page summary of my master thesis which I handed in on June 15, 2019 at TUM. The thesis takes the form of a literature review on the existing rigorous analysis on neural networks. It focuses on 3 key aspects: modern and classical results in approximation theory, robustness of neural networks and unique identification of neural network weights. The thesis was supervised by Prof. Dr. Massimo Fornasier at the Chair of Applied Numerical Analysis of the Mathematics Department at TUM.
The document discusses fuzzy logic and its applications in control systems. It explains that fuzzy logic resembles human decision making by generating solutions from uncertain information. Fuzzy logic was developed by Lotfi Zadeh to allow computers to handle vague data like human ideas. Fuzzy sets differ from classical sets by having gradual boundaries. Fuzzy control uses fuzzy rules and membership functions to mimic how humans control systems. Fuzzy logic has been used widely in applications like washing machines and car controls. The future applications of fuzzy logic are vast as it can handle any problem involving human intuition.
This presentation discusses the application of integration. It defines integration as calculating the definite or indefinite integral of a function over a specified interval or range of values. Several techniques for integration are presented, including trigonometric substitution which is used when a problem involves square roots with two terms under the radical. Real-life applications of integration are also mentioned, such as alternative approaches to quantum mechanics, high energy physics, and condensed matter field theories.
This document discusses using repeated simulations of a crisp neural network to obtain quasi-fuzzy weight sets (QFWS) that can be used to initialize fuzzy neural networks. The key points are:
1) A crisp neural network is repeatedly trained on input-output data to model an unknown function. The connection weights change with each simulation.
2) Recording the weights from multiple simulations produces quasi-fuzzy weight sets, where each weight is a fuzzy set rather than a single value.
3) These QFWS can provide initial solutions for training type-I fuzzy neural networks with reduced computational complexity compared to random initialization.
4) The QFWS follow fuzzy arithmetic and allow both numerical and linguistic data to
Functional brain parcellations break the brain into modules of regions with similar connectivity profiles. Parcellations exist over a range of scales from large scale networks to smaller specialized cortical areas. Larger parcels have higher homogeneity while stability is highest at the group level and more variable at the individual level. A number of metrics are used to evaluate parcellations including homogeneity, stability, and agreement with functional tasks. Individual parcellations provide more detail than group parcellations but estimating them jointly improves accuracy. Gradients provide an alternative to parcels by describing continuous connectivity patterns in the brain.
This document summarizes a study that compares fuzzy logic and neuro-fuzzy models for predicting direct current in motors. Fuzzy logic and neuro-fuzzy systems were used to model the relationship between motor torque, power, speed (inputs) and current (output). Both techniques were tested on a dataset of 507 samples. The neuro-fuzzy inference system (ANFIS) performed slightly better than the fuzzy logic system at predicting motor current, demonstrating the benefits of combining fuzzy logic with neural networks.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
A Learning Linguistic Teaching Control for a Multi-Area Electric Power SystemCSCJournals
This paper presents a new methodology for designing a neuro-fuzzy control for complex physical systems. By developing a Neural -Fuzzy system learning with linguistic teaching signals. The advantage of this technique is that, produce a simple and well-performing system because it selects the fuzzy sets and the numerical numbers and process both numerical and linguistic information. This approach is able to process and learn numerical information as well as linguistic information. The proposed control scheme is applied to a multi-area power system with hydraulic and thermal turbines.
One page summary of master thesis "Mathematical Analysis of Neural Networks"Alina Leidinger
This is a one page summary of my master thesis which I handed in on June 15, 2019 at TUM. The thesis takes the form of a literature review on the existing rigorous analysis on neural networks. It focuses on 3 key aspects: modern and classical results in approximation theory, robustness of neural networks and unique identification of neural network weights. The thesis was supervised by Prof. Dr. Massimo Fornasier at the Chair of Applied Numerical Analysis of the Mathematics Department at TUM.
The document discusses fuzzy logic and its applications in control systems. It explains that fuzzy logic resembles human decision making by generating solutions from uncertain information. Fuzzy logic was developed by Lotfi Zadeh to allow computers to handle vague data like human ideas. Fuzzy sets differ from classical sets by having gradual boundaries. Fuzzy control uses fuzzy rules and membership functions to mimic how humans control systems. Fuzzy logic has been used widely in applications like washing machines and car controls. The future applications of fuzzy logic are vast as it can handle any problem involving human intuition.
This presentation discusses the application of integration. It defines integration as calculating the definite or indefinite integral of a function over a specified interval or range of values. Several techniques for integration are presented, including trigonometric substitution which is used when a problem involves square roots with two terms under the radical. Real-life applications of integration are also mentioned, such as alternative approaches to quantum mechanics, high energy physics, and condensed matter field theories.
There are very few examples of the use of various architectures for recurrent neural
networks to predict student learning outcomes. In fact, the only architecture used to
solve this problem is the LSTM architecture. In the works devoted to the use of LSTM
to predict educational outcomes, the results of a detailed theoretical substantiation of
the preference of this particular architecture of the RNN are not presented. In this
regard, it seems advisable to provide such justification in the framework of this study.
The main property of input data for prediction of educational outcomes is its
temporary nature. Some sequence of user actions unfolds in time and is evaluated
(classified) by an external observer as evidence of the presence or absence of an
educational result (objective or metaobjective). In this regard, the RNN used to classify
user actions should perform a procedure for adjusting the weights of neurons for a
certain set of states in the past. At the same time, the length of the sequence of these
states is not predetermined: it can be both short (for example, for objective results),
and quite long.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
The document discusses the brain as a complex network and introduces the concept of the connectome. It describes how the brain exhibits both segregation into specialized areas and integration through connections between areas. Mapping the structural and functional connectivity between brain regions using tools from network science and graph theory provides a powerful way to quantitatively describe the topological organization of the brain. Analyzing the human connectome has revealed hierarchical modular organization and the presence of connector hubs and rich clubs that facilitate integration and efficient communication in the brain network. Understanding disruptions to functional and structural connectivity may help explain neurological and psychiatric disorders.
This document discusses using the Levenberg-Marquardt algorithm for forecasting stock exchange share rates on the Karachi Stock Exchange. It provides an overview of artificial neural networks and how they can be used for financial forecasting applications. The Levenberg-Marquardt algorithm is presented as an efficient method for training neural networks to minimize errors through gradient descent. The document applies this method to train a neural network to predict the direction of change in share prices on the Karachi Stock Exchange. The network is trained on historical stock price data and testing shows it can achieve the performance goal of forecasting next day price changes.
This research article proposes accelerating a geodesic ray-tracing algorithm for fiber tracking in brain imaging using parallel programming on a GPU. Fiber tracking uses diffusion MRI to noninvasively examine brain fiber structures at a microscopic level. While geodesic ray-tracing is robust, it is computationally expensive to reliably find all fibers between seed points and target regions. The authors implemented a highly parallel version of the algorithm using NVIDIA's CUDA platform on a GPU. This provided a significant reduction in running time of up to 40x compared to a multithreaded CPU implementation, greatly increasing the applicability of the algorithm.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Soft computing is a set of computational techniques that aim to mimic human-like reasoning and decision making. The main techniques are fuzzy logic, neural networks, evolutionary computing, machine learning, and probabilistic reasoning. Each technique has strengths and weaknesses, but they complement each other. When used together, soft computing techniques can solve complex problems that are difficult for traditional mathematical methods. The paper reviews these soft computing techniques and explores how they could be applied to problems in various domains.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
Building Neural Network Through Neuroevolutionbergel
Neuroevolution is a technique that uses genetic algorithms to train neural networks. It evolves network topologies, weights, and hyperparameters without relying on backpropagation or large labeled training datasets. The NEAT algorithm is introduced as a prominent neuroevolution method. It uses speciation to protect innovative network topologies and incremental evolution. NEAT encodes networks and uses historical markings to solve the competing conventions problem during crossover. Extensions to NEAT aim to further improve the scalability and efficiency of neuroevolution.
This document provides an overview of self-organizing maps (SOMs), a type of artificial neural network. It discusses the biological motivation for SOMs, which are inspired by self-organizing systems in the brain. The document outlines the basic architecture and learning algorithm of SOMs, including initialization, training procedures, and classification. It also reviews various properties of SOMs, such as their ability to approximate input spaces and perform topological ordering and density matching. Finally, applications of SOMs are briefly mentioned, such as for speech recognition, image analysis, and data visualization.
Soft computing is an area of study that deals with imprecise or uncertain data using techniques like neural networks, fuzzy logic, and evolutionary computation. Unlike conventional computing which seeks exactness, soft computing is tolerant of imprecision and approximation to achieve tractability and robust solutions. The key components of soft computing aim to emulate aspects of human cognition by using neural networks for learning, fuzzy logic for modeling uncertainty, and evolutionary algorithms for optimization. Soft computing has many successful applications and its influence is growing in science, engineering, and other fields.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
This document provides an introduction to artificial intelligence using fuzzy logic and neural networks. It discusses key concepts such as fuzzy logic, which allows for partial set membership rather than binary logic, and neural networks, which are modeled off the human brain. The document also introduces fuzzy-neural hybrid networks, which combine fuzzy logic and neural networks to leverage the strengths of both approaches. Examples of applications include pattern recognition, data mining, and control systems.
Complexity and Quantum Information ScienceMelanie Swan
This document discusses using quantum information science and quantum computing to model complex systems like the human brain. It proposes the "AdS/Brain Theory of Neural Signaling" which uses wavefunctions, tensor networks, and neural field theories at different scales from brain networks to molecules. Quantum computing could provide a new platform to model the brain across its nine orders of magnitude of complexity and help complete the human connectome by handling the large data and processing requirements. The AdS/Brain theory represents the first application of the AdS/CFT correspondence across multiple scales of the brain.
ANALYSIS ON MACHINE CELL RECOGNITION AND DETACHING FROM NEURAL SYSTEMSIAEME Publication
This document summarizes an analysis on machine cell recognition and detaching from neural systems. It discusses using artificial neural networks (ANNs) like the backpropagation network, self-organizing map network, and ART1 network to identify machine cells and facilitate cellular manufacturing. The document provides background on neural networks and cellular manufacturing. It discusses using unsupervised ANN methods like FLANN (fast learning artificial neural network) to cluster machines and optimize production. The goal is to minimize the number of parts produced in each cell to improve efficiency.
Automatic Differentiation and SciML in Reality: What can go wrong, and what t...Chris Rackauckas
How does automatic differentiation work, what happens when you apply it to equation solvers, and how can it go wrong? This talk is all about the details of how scientific machine learning (SciML) works. It goes into detail as to how neural networks are trained in the context of equation solvers, along with the numerical issues that can arise in the differentiation processes.
https://sciml.ai/
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
The document discusses the differences between machine learning (ML), statistical learning, data mining (DM), and automated learning (AL). It argues that while ML and statistical learning developed similar techniques starting in the 1960s, DM emerged in the 1990s from a merging of database research and automated learning. However, industry was much more enthusiastic about adopting DM techniques compared to AL techniques, even though many DM systems are just friendly interfaces of AL systems. The document aims to explain the key differences between DM and AL that led to DM's greater commercial success.
There are very few examples of the use of various architectures for recurrent neural
networks to predict student learning outcomes. In fact, the only architecture used to
solve this problem is the LSTM architecture. In the works devoted to the use of LSTM
to predict educational outcomes, the results of a detailed theoretical substantiation of
the preference of this particular architecture of the RNN are not presented. In this
regard, it seems advisable to provide such justification in the framework of this study.
The main property of input data for prediction of educational outcomes is its
temporary nature. Some sequence of user actions unfolds in time and is evaluated
(classified) by an external observer as evidence of the presence or absence of an
educational result (objective or metaobjective). In this regard, the RNN used to classify
user actions should perform a procedure for adjusting the weights of neurons for a
certain set of states in the past. At the same time, the length of the sequence of these
states is not predetermined: it can be both short (for example, for objective results),
and quite long.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
The document discusses the brain as a complex network and introduces the concept of the connectome. It describes how the brain exhibits both segregation into specialized areas and integration through connections between areas. Mapping the structural and functional connectivity between brain regions using tools from network science and graph theory provides a powerful way to quantitatively describe the topological organization of the brain. Analyzing the human connectome has revealed hierarchical modular organization and the presence of connector hubs and rich clubs that facilitate integration and efficient communication in the brain network. Understanding disruptions to functional and structural connectivity may help explain neurological and psychiatric disorders.
This document discusses using the Levenberg-Marquardt algorithm for forecasting stock exchange share rates on the Karachi Stock Exchange. It provides an overview of artificial neural networks and how they can be used for financial forecasting applications. The Levenberg-Marquardt algorithm is presented as an efficient method for training neural networks to minimize errors through gradient descent. The document applies this method to train a neural network to predict the direction of change in share prices on the Karachi Stock Exchange. The network is trained on historical stock price data and testing shows it can achieve the performance goal of forecasting next day price changes.
This research article proposes accelerating a geodesic ray-tracing algorithm for fiber tracking in brain imaging using parallel programming on a GPU. Fiber tracking uses diffusion MRI to noninvasively examine brain fiber structures at a microscopic level. While geodesic ray-tracing is robust, it is computationally expensive to reliably find all fibers between seed points and target regions. The authors implemented a highly parallel version of the algorithm using NVIDIA's CUDA platform on a GPU. This provided a significant reduction in running time of up to 40x compared to a multithreaded CPU implementation, greatly increasing the applicability of the algorithm.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Soft computing is a set of computational techniques that aim to mimic human-like reasoning and decision making. The main techniques are fuzzy logic, neural networks, evolutionary computing, machine learning, and probabilistic reasoning. Each technique has strengths and weaknesses, but they complement each other. When used together, soft computing techniques can solve complex problems that are difficult for traditional mathematical methods. The paper reviews these soft computing techniques and explores how they could be applied to problems in various domains.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
Building Neural Network Through Neuroevolutionbergel
Neuroevolution is a technique that uses genetic algorithms to train neural networks. It evolves network topologies, weights, and hyperparameters without relying on backpropagation or large labeled training datasets. The NEAT algorithm is introduced as a prominent neuroevolution method. It uses speciation to protect innovative network topologies and incremental evolution. NEAT encodes networks and uses historical markings to solve the competing conventions problem during crossover. Extensions to NEAT aim to further improve the scalability and efficiency of neuroevolution.
This document provides an overview of self-organizing maps (SOMs), a type of artificial neural network. It discusses the biological motivation for SOMs, which are inspired by self-organizing systems in the brain. The document outlines the basic architecture and learning algorithm of SOMs, including initialization, training procedures, and classification. It also reviews various properties of SOMs, such as their ability to approximate input spaces and perform topological ordering and density matching. Finally, applications of SOMs are briefly mentioned, such as for speech recognition, image analysis, and data visualization.
Soft computing is an area of study that deals with imprecise or uncertain data using techniques like neural networks, fuzzy logic, and evolutionary computation. Unlike conventional computing which seeks exactness, soft computing is tolerant of imprecision and approximation to achieve tractability and robust solutions. The key components of soft computing aim to emulate aspects of human cognition by using neural networks for learning, fuzzy logic for modeling uncertainty, and evolutionary algorithms for optimization. Soft computing has many successful applications and its influence is growing in science, engineering, and other fields.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
This document provides an introduction to artificial intelligence using fuzzy logic and neural networks. It discusses key concepts such as fuzzy logic, which allows for partial set membership rather than binary logic, and neural networks, which are modeled off the human brain. The document also introduces fuzzy-neural hybrid networks, which combine fuzzy logic and neural networks to leverage the strengths of both approaches. Examples of applications include pattern recognition, data mining, and control systems.
Complexity and Quantum Information ScienceMelanie Swan
This document discusses using quantum information science and quantum computing to model complex systems like the human brain. It proposes the "AdS/Brain Theory of Neural Signaling" which uses wavefunctions, tensor networks, and neural field theories at different scales from brain networks to molecules. Quantum computing could provide a new platform to model the brain across its nine orders of magnitude of complexity and help complete the human connectome by handling the large data and processing requirements. The AdS/Brain theory represents the first application of the AdS/CFT correspondence across multiple scales of the brain.
ANALYSIS ON MACHINE CELL RECOGNITION AND DETACHING FROM NEURAL SYSTEMSIAEME Publication
This document summarizes an analysis on machine cell recognition and detaching from neural systems. It discusses using artificial neural networks (ANNs) like the backpropagation network, self-organizing map network, and ART1 network to identify machine cells and facilitate cellular manufacturing. The document provides background on neural networks and cellular manufacturing. It discusses using unsupervised ANN methods like FLANN (fast learning artificial neural network) to cluster machines and optimize production. The goal is to minimize the number of parts produced in each cell to improve efficiency.
Automatic Differentiation and SciML in Reality: What can go wrong, and what t...Chris Rackauckas
How does automatic differentiation work, what happens when you apply it to equation solvers, and how can it go wrong? This talk is all about the details of how scientific machine learning (SciML) works. It goes into detail as to how neural networks are trained in the context of equation solvers, along with the numerical issues that can arise in the differentiation processes.
https://sciml.ai/
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
The document discusses the differences between machine learning (ML), statistical learning, data mining (DM), and automated learning (AL). It argues that while ML and statistical learning developed similar techniques starting in the 1960s, DM emerged in the 1990s from a merging of database research and automated learning. However, industry was much more enthusiastic about adopting DM techniques compared to AL techniques, even though many DM systems are just friendly interfaces of AL systems. The document aims to explain the key differences between DM and AL that led to DM's greater commercial success.
An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.
This article provides an introduction to artificial neural networks (ANNs) and presents guidelines for designing effective ANN solutions. It discusses the key components of ANNs, including their biological inspiration, history, and different types of learning algorithms. The article emphasizes that successful ANN development requires extensive domain knowledge engineering and following best practices for selecting input variables, learning methods, architecture, and training samples. Specifically, it recommends knowledge-based input selection, choosing appropriate learning algorithms based on the data type, designing network topology based on the algorithm, and selecting optimal training set sizes, especially for time series problems. Overall, the article stresses that incorporating domain expertise at each design step is essential for building ANNs that generalize well to new problems.
Cerebellar Model Controller with new Model of Granule Cell-golgi Cell Buildi...IJECEIAES
Processing in the cerebellum is roughly described as feed forward processing of incoming information over three layers of the cerebellar cortex that send intermediate output to deep cerebellar nuclei, the only output from the cerebellum. Beside this main picture there are several feedback routes, mainly not included in models. In this paper we use new model for neuronal circuit of the cerebellar granule cell layer, as collection of idealized granule cell–golgi cell building blocks with capability of generating multi-dimensional receptive fields modulated by separate input coming to lower dendrite tree of Golgi cell. Resulting cerebellar model controller with two-phase learning will acquire multitude of generalization capabilities when used as robot joint controller. This will usually require more than one Purkinje cell per output. Functionality of granule cell-Golgi cell building block was evaluated with simulations using Simulink single compartment spiking neuronal model. Trained averaging cerebellar model controller attains very good tracking results for wide range of unlearned slower and faster trajectories, with additional improvements by relearning at faster trajectories. Inclusion of new dynamical effects to the controller results with linear growth in complexity for inputs targeting lower dendrite tree of Golgi cell, important for control applications in robotics, but not only.
Neural networks are a type of machine learning algorithm inspired by the human brain. They are composed of interconnected nodes that process input data and pass signals to other nodes. Neural networks learn by adjusting the weights between nodes during training to minimize errors and improve accuracy over time. Common types of neural networks include perceptrons and multilayer feedforward networks. The history of neural networks began in the 1940s and saw major developments like the perceptron in the 1950s and the introduction of backpropagation in the 1970s, which enabled modern deep learning applications.
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
BASIC HYDROPONIC SYSTEM
HYDROPONIC GROW MEDIA
LIST OF CROPS
ADVANTAGES OF HYDROPONIC TECHNOLOGY
DISADVANTAGES OF HYDROPONIC TECHNOLOGY
FUTURE SCOPE OF HYDROPONIC TECHNOLOGY
CASE STUDY
CONCLUSION
REFERENCES
MATERIAL MANAGEMENT AND HUMAN RESOURCE MANAGEMENTAishwarya Phalke
Material flow system
Role of material management in construction management
Vendor networking
Logistic & supply chain management
Role of ERP
Human resource in the construction sector
Staffing policy & pattern
HR management process
Performance appraisal& job evaluation
Training and career planning
Material codification
ELEMENTS OF RISK MANAGEMENT AND VALUE ENGINEERINGAishwarya Phalke
RISK MANAGEMENT
TYPES OF RISK
STEPS INVOLVE IN RISK
Identification of risk
Risk management
Risk mitigation
Risk monitoring
ROLE OF RISK MANAGER
BENEFITS OF VALUE ENGINEERING
SOURCES OF ENERGY
What’s Labour Law
Origins of Labour Laws
Individual Labour Law
Labour Policy in India
Duties of Employer under the Act
Working Hours under the Act
Important Acts of Indian Labour Law
Construction Scheduling, Work Study & Work Measurement Aishwarya Phalke
DEFINITION OF PROJECT SCHEDULING
KEY POINTS OF SCHEDULING
PURPOSE OF SCHEDULING
Internal factors affecting scheduling
Work Breakdown Structure Diagram
LINE OF BALANCE
ADVANTAGES OF LOB
WORK STUDY
Role of Work-Study
Objectives of Work-Study
BASIC PROCEDURE OF WORK STUDY
METHOD STUDY ( MOTION STUDY)
Flow chart of method study
The document discusses construction management. It notes that the construction industry contributes significantly to the Indian economy and GDP. Construction management is important for large-scale capital projects to control time, cost, and quality. Effective construction management is necessary for economic growth. Construction projects require managing many resources and involve various disciplines, making management and planning essential. Reasons for project overruns include poor cost estimates, resource planning, and schedule management. The document also outlines Henri Fayol's 14 principles of management.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
2. A. Artificial neural network
Introduction
“Neural networks are parallel computing devices, which is basically an attempt to make a
computer model of the brain. The main objective is to develop a system to perform various
computational tasks faster than the traditional systems. These tasks include pattern
recognition and classification, approximation, optimization, and data clustering.”
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3. What is Artificial Neural Network?
It is a computational system inspired by the
Structure
Processing Method
Learning Ability of a biological brain
A large number of very simple processing neuron-like processing elements A large number
of weighted connections between the elements Distributed representation of knowledge
over the connections Knowledge is acquired by network through a learning process
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6. B. Fuzzy logic
Introduction
Fuzzy concepts first introduced by Zadeh in the 1960s and 70s
Traditional computational logic and set theory is all about :-
true or false
zero or one
in or out (in terms of set membership)
black or white (no grey)
Not the case with fuzzy logic and fuzzy sets!
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7. Formal Fuzzy Logic
Fuzzy logic can be seen as an extension of ordinary logic, where the main difference is that
we use fuzzy sets for the membership of a variable
We can have fuzzy propositional logic and fuzzy predicate logic
Fuzzy logic can have many advantages over ordinary logic in areas like artificial
intelligence where a simple true/false statement is insufficient
Simple Fuzzy Operators
o As described by Zadeh (1973).NOT X = 1 - µX (y). e.g. 0.8 cold → (1 – 0.8) = 0.2 NOT
cold
o X OR Y (union) = max(µX (y), µY (y)). e.g. 0.8 cold, 0.5 rainy → 0.8 cold OR rainy
o X AND Y (intersection) = min(µX (y), µY (y)). e.g. 0.9 hot, 0.7 humid → 0.7 hot AND
humid
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8. Fuzzy System Overview
When making inferences, we want to clump the continuous numerical values into sets
Unlike Boolean logic, fuzzy logic uses fuzzy sets rather than crisp sets to determine the
membership of a variable
This allows values to have a degree of membership with a set, which denotes the extent to
which a proposition is true
The membership function may be triangular, trapezoidal, Gaussian or any other shape
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9. Application
Structural analysis and Design for structural optimization and optimum Design of
structures.
The field of Hydrology & Water Resource engineering.
Traffic engineering.
Reliability of structures.
Metal structures.
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11. C. Genetic algorithm
Introduction
“Growing specialization and diversification have brought a host of monographs and textbooks
on increasingly specialized topics. However, the “tree” of knowledge of mathematics and
related fields does not grow only by putting forth new branches. It also happens, quite often
in fact, that branches which were thought to be completely disparate are suddenly seen to
be related”
Michiel Hazewinkel
Applying mathematics to a problem of the real world mostly means, at first, modeling the
problem mathematically, maybe with hard restrictions, idealizations, or simplifications,
then solving the mathematical problem, and finally drawing conclusions about the real
problem based on the solutions of the mathematical problem.
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12. Components, Structure, & Terminology
Since genetic algorithms are designed to simulate a biological process, much of the
relevant terminology is borrowed from biology. However, the entities that this terminology
refers to in genetic algorithms are much simpler than their biological counterparts.
The basic components common to almost all genetic algorithms are:
a fitness function for optimization
a population of chromosomes
selection of which chromosomes will reproduce
crossover to produce next generation of chromosomes
random mutation of chromosomes in new generation
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13. Application
1) Resource Leveling.
2) Scheduling Of Large Projects.
3) Resource Constraints.
4) A Solution To The Scheduling Problem.
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