soft computing manoj
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Soft computing is a field that uses approximate solutions and techniques like fuzzy logic, neural networks, and evolutionary computation to problems that are too complex for traditional binary logic-based computing. It aims to achieve human-like decision making by incorporating uncertainty, imprecision, and partial truth into solutions. The main goal of soft computing is to develop intelligent machines that can provide solutions to real-world problems that are difficult to model mathematically.
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Soft computing
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
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soft-computing
Artificial intelligence is a field of study that uses computational techniques to simulate human intelligence processes like learning, reasoning, and problem solving. It includes approaches like expert systems, neural networks, genetic algorithms, fuzzy logic systems, and swarm intelligence methods. The goal is to develop tools that can perform tasks requiring human-level intelligence.
Introduction to Soft Computing
Soft computing is an approach to building computationally intelligent systems that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and evolutionary computation. These techniques were developed to mimic human-like intelligence by accommodating imprecision and exploiting uncertainty. Soft computing is used to build intelligent systems that can learn and adapt to new environments. Neuro-fuzzy systems combine neural networks and fuzzy logic to create adaptive and knowledge-based intelligent systems.
Soft computing
This document discusses soft computing techniques. Soft computing deals with inexact solutions and uses techniques like fuzzy systems, neural networks, machine learning, and probabilistic reasoning. It summarizes the key differences between hard and soft computing, providing examples of each technique. The document concludes by listing some common applications of soft computing such as handwriting recognition, image processing, and decision support systems.
Soft computing01
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
Basics of Soft Computing
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
Soft computing abstracts
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.
An Introduction to Soft Computing
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
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Soft computing
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
soft-computing
Artificial intelligence is a field of study that uses computational techniques to simulate human intelligence processes like learning, reasoning, and problem solving. It includes approaches like expert systems, neural networks, genetic algorithms, fuzzy logic systems, and swarm intelligence methods. The goal is to develop tools that can perform tasks requiring human-level intelligence.
Introduction to Soft Computing
Soft computing is an approach to building computationally intelligent systems that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and evolutionary computation. These techniques were developed to mimic human-like intelligence by accommodating imprecision and exploiting uncertainty. Soft computing is used to build intelligent systems that can learn and adapt to new environments. Neuro-fuzzy systems combine neural networks and fuzzy logic to create adaptive and knowledge-based intelligent systems.
Soft computing
This document discusses soft computing techniques. Soft computing deals with inexact solutions and uses techniques like fuzzy systems, neural networks, machine learning, and probabilistic reasoning. It summarizes the key differences between hard and soft computing, providing examples of each technique. The document concludes by listing some common applications of soft computing such as handwriting recognition, image processing, and decision support systems.
Soft computing01
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
Basics of Soft Computing
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
Soft computing abstracts
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.
An Introduction to Soft Computing
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
Soft Computing
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
Soft Computing
This document discusses neuro fuzzy systems and soft computing. It provides the following key points:
1. Neuro-fuzzy systems combine fuzzy logic and neural networks, allowing the system to learn from data and maintain interpretable fuzzy rules. It can be viewed as a 3-layer neural network with fuzzy rules in the hidden layer.
2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
3. Soft computing constituents include neural networks, fuzzy sets, approximate reasoning, and derivative-free optimization methods like genetic algorithms and simulated annealing. These work together to enable learning
Introduction to Soft Computing
Soft computing is an approach to engineering that is inspired by nature. It includes techniques like fuzzy logic, probabilistic reasoning, evolutionary computation, neural networks, and machine learning. These techniques are useful for problems that are too complex or undefined for conventional analytical or hard computing techniques. Soft computing provides approximate solutions and can handle imprecise data. It has applications in areas like robotics, artificial intelligence, and machine translation.
Soft computing
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
Application of soft computing techniques in electrical engineering
This document discusses the application of soft computing techniques in electrical engineering. It begins with an introduction to soft computing and its key elements including fuzzy logic, neural networks, evolutionary computation, machine learning and probabilistic reasoning. It then discusses hard computing versus soft computing, defining hard computing as requiring precise analytical models and definitions, while soft computing can handle imprecision. The document outlines several soft computing techniques - neural networks, fuzzy logic, and their applications in power system economic load dispatch and generation level determination to solve complex, non-linear optimization problems in electrical engineering. In conclusion, soft computing provides alternatives to traditional techniques for electrical engineering problems involving uncertainty.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Introduction to soft computing
Present introductory level to soft computing.
Briefly present about neural network, fuzzy logic and evolutionary optimization.
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The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
Soft computing
This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.
Introduction to soft computing
This document provides an introduction to soft computing. It discusses intelligent systems and how traditional approaches use mathematical models and rule-based systems. Soft computing aims to mimic human reasoning using fuzzy systems, neural networks, evolutionary computing, and probabilistic reasoning. Soft computing is tolerant of imprecision, uncertainty, partial truths, and approximations. It has advantages over hard computing by being closer to human thinking and using linguistic models that are simple, comprehensible, and fast. Soft computing has become widely used with over 24,000 publications to date.
SoftComputing1
1. Soft computing is a branch of artificial intelligence that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and probabilistic reasoning.
2. Soft computing is used to build intelligent systems that exhibit traits of adaptability and knowledge. It combines neural networks, which recognize patterns and adapt, with fuzzy inference systems, which incorporate human knowledge for decision making.
3. Recent developments in soft computing include applications in image processing, remote sensing, and data mining techniques like swarm intelligence and diffusion processes.
Soft Computing-173101
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Kiran computer
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
Soft computing
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
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Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
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Soft computing is a field that uses inexact solutions for problems without exact solutions. It deals with imprecision, uncertainty, and approximation rather than precise values. Soft computing techniques include fuzzy systems, neural networks, evolutionary computation, machine learning, and probabilistic reasoning. These were developed starting in the 1940s and are useful for tasks like control, decision making, and pattern recognition. Soft computing is applied in areas like robotics, home appliances, automobiles, and spacecraft.
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Soft Computing
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
Soft Computing
This document discusses neuro fuzzy systems and soft computing. It provides the following key points:
1. Neuro-fuzzy systems combine fuzzy logic and neural networks, allowing the system to learn from data and maintain interpretable fuzzy rules. It can be viewed as a 3-layer neural network with fuzzy rules in the hidden layer.
2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
3. Soft computing constituents include neural networks, fuzzy sets, approximate reasoning, and derivative-free optimization methods like genetic algorithms and simulated annealing. These work together to enable learning
Introduction to Soft Computing
Soft computing is an approach to engineering that is inspired by nature. It includes techniques like fuzzy logic, probabilistic reasoning, evolutionary computation, neural networks, and machine learning. These techniques are useful for problems that are too complex or undefined for conventional analytical or hard computing techniques. Soft computing provides approximate solutions and can handle imprecise data. It has applications in areas like robotics, artificial intelligence, and machine translation.
Soft computing
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
Application of soft computing techniques in electrical engineering
This document discusses the application of soft computing techniques in electrical engineering. It begins with an introduction to soft computing and its key elements including fuzzy logic, neural networks, evolutionary computation, machine learning and probabilistic reasoning. It then discusses hard computing versus soft computing, defining hard computing as requiring precise analytical models and definitions, while soft computing can handle imprecision. The document outlines several soft computing techniques - neural networks, fuzzy logic, and their applications in power system economic load dispatch and generation level determination to solve complex, non-linear optimization problems in electrical engineering. In conclusion, soft computing provides alternatives to traditional techniques for electrical engineering problems involving uncertainty.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAS
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Introduction to soft computing
Present introductory level to soft computing.
Briefly present about neural network, fuzzy logic and evolutionary optimization.
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima Pandit
The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
Soft computing
This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.
Introduction to soft computing
This document provides an introduction to soft computing. It discusses intelligent systems and how traditional approaches use mathematical models and rule-based systems. Soft computing aims to mimic human reasoning using fuzzy systems, neural networks, evolutionary computing, and probabilistic reasoning. Soft computing is tolerant of imprecision, uncertainty, partial truths, and approximations. It has advantages over hard computing by being closer to human thinking and using linguistic models that are simple, comprehensible, and fast. Soft computing has become widely used with over 24,000 publications to date.
SoftComputing1
1. Soft computing is a branch of artificial intelligence that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and probabilistic reasoning.
2. Soft computing is used to build intelligent systems that exhibit traits of adaptability and knowledge. It combines neural networks, which recognize patterns and adapt, with fuzzy inference systems, which incorporate human knowledge for decision making.
3. Recent developments in soft computing include applications in image processing, remote sensing, and data mining techniques like swarm intelligence and diffusion processes.
Soft Computing-173101
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Kiran computer
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
Soft computing
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
Emotion detection using cnn.pptx
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Unit I & II in Principles of Soft computing
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
Soft computing approach to control system
Control systems engineering applies control theory to design systems that achieve desired behaviors. Soft computing techniques, such as fuzzy logic, neural networks, and genetic algorithms, resemble biological processes more closely than traditional techniques for solving computationally difficult tasks. This document presents a case study on using a fuzzy logic controller for speed control of a DC motor. Simulation results show the fuzzy logic controller provides better performance than PID controllers, with faster rise time, shorter settling time, and no overshoot. Soft computing approaches thus provide effective intelligent control systems with advantages like not requiring complex math and giving real-time expert control.
Machine Learning techniques
This document discusses machine learning and artificial intelligence. It provides an overview of the machine learning process, including obtaining raw data, preprocessing the data, applying algorithms to extract features and train models, and generating outputs. It then describes different types of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Specific algorithms like artificial neural networks, support vector machines, genetic algorithms are also briefly explained. Real-world applications of machine learning like character recognition and medical diagnosis are listed.
Neural networks and deep learning
Neural networks and deep learning are machine learning techniques inspired by the human brain. Neural networks consist of interconnected nodes that process input data and pass signals to other nodes. The main types discussed are artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). ANNs can learn nonlinear relationships between inputs and outputs. CNNs are effective for image processing by learning relevant spatial features. RNNs capture sequential dependencies in data like text. Deep learning uses neural networks with many layers to learn complex patterns in large datasets.
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Application of soft computing techniques in electrical engineering
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Soft computing is a field that uses inexact solutions for problems without exact solutions. It deals with imprecision, uncertainty, and approximation rather than precise values. Soft computing techniques include fuzzy systems, neural networks, evolutionary computation, machine learning, and probabilistic reasoning. These were developed starting in the 1940s and are useful for tasks like control, decision making, and pattern recognition. Soft computing is applied in areas like robotics, home appliances, automobiles, and spacecraft.
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Gisvdlsigsbs zkdvdkod kaisvdv kshvdkd h kya hua hai kya hua hai kya hua hai kya hua h tere ko kuch bolna nahi kuch bhi ho raha h kya hai na dhund ke bta do ki nahi kuch puchungiii ho gya h kya hai ki h kya kr rhe h h h h ki mil jayega aur kya ho raha hai ki aap recharge ho jayega aur ek baat ka gussa h kya kar rahe hain aap bataiye ho gai thi na kal ke sath se ho gai hai aap bataiye ho ke aap hi nhi h kya kar rahe hain to aap bataiye hai aur aap hi ho jayegaa se baat karte hain aap ko free h kya aap bataiye hai aap ko bhi free nhi ho skta h tu bhi kuch share kar ye
Soft Computing: A survey
Soft Computing is the fusion of methodologies that were designed to model and enable
solutions to real world problems, which are not modeled or too difficult to model, mathematically. Soft
computing is a consortium of methodologies that works synergistically and provides, in one form or
another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to
exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to
achieve tractability, robustness and low-cost solutions. The guiding principle is to devise methods of
computation that lead to an acceptable solution at low cost, by seeking for an approximate solution to an
imprecisely or precisely formulated problem.Soft Computing (SC) represents a significant paradigm shift
in the aims of computing, which reflects the fact that the human mind, unlike present day computers,
possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain
and lacking in categoricity. At this juncture, the principal constituents of Soft Computing (SC) are: Fuzzy
Systems (FS), including Fuzzy Logic (FL); Evolutionary Computation (EC), including Genetic
Algorithms (GA); Neural Networks (NN), including Neural Computing (NC); Machine Learning (ML);
and Probabilistic Reasoning (PR). In this paper we focus on fuzzy methodologies and fuzzy systems, as
they bring basic ideas to other SC methodologies
Artificial intelligence
Artificial intelligence (AI) is software that allows computers and robots to perform tasks in a way that mimics human intelligence. John McCarthy first proposed the term "artificial intelligence" in 1956. AI uses techniques like machine learning, natural language processing, and computer vision to perform tasks previously only done by humans, such as playing games, recognizing speech, and understanding language. While AI has advantages like efficiency, reliability, and ability to handle complex tasks, it also has drawbacks like limited ability and lack of complete human traits. The ultimate goal of AI research is to solve problems humans cannot.
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Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. It includes techniques like neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that cannot be easily modeled mathematically and to achieve human-like decision making despite uncertainty. Soft computing has been applied in various domains like consumer appliances, robotics, and game playing.
Machine Learning
These slides are from a presentation on understanding Machine Learning at a high level. The talk touches on linear regression, neural networks, and how Deep Learning fits into Machine Learning.
AI and Expert Systems
This document provides an overview of artificial intelligence and expert systems. It discusses key concepts in artificial intelligence including machine learning, problem solving, and visual processing. The major branches of AI are described as perceptive systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as storing knowledge and making inferences. The components, advantages, and applications of expert systems are also summarized.
Deep learning
This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.
AI Presentation 1
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
Cognitive computing
The document discusses cognitive computing, which relies on techniques like expert systems, statistics, and mathematical models to mimic human reasoning. Cognitive computing systems can handle uncertainties and complex problems through experience and learning. The key aspects are:
- Cognitive computing represents self-learning systems that use machine learning models to mimic the human brain.
- The "brain" of cognitive systems is the neural network, which underlies deep learning.
- Cognitive computing aims to simulate human thought to solve complex problems through data analysis, pattern recognition, and natural language processing like the human brain.
Numenta ACM Data Min - PowerPoint Presentation
This document provides an overview of Hierarchical Temporal Memory (HTM) and Numenta. It discusses how HTM is inspired by neuroscience and aims to build a common cortical algorithm. HTM forms a hierarchical network of nodes that learns spatial patterns and temporal sequences to build a model of input data. The document describes Numenta's timeline and demos applying HTM to tasks like object recognition, web analytics, and biomedical imaging. Potential applications of HTM include web analytics, video analysis, fraud detection and more.
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Radiology has historically been a leader in digital transformation in healthcare through the introduction of technologies like PACS and teleradiology. Radiology is now at another crossroads with new digital imaging technologies and there is potential for it to evolve into an integrated diagnostic service. Recent decades have seen the adoption of many new digital imaging modalities and pictures were initially printed but as technology improved, radiology has converted to a filmless digital environment. There is now significant interest in machine learning and artificial intelligence to help analyze medical images and aid radiologists.
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The document discusses artificial intelligence (AI), including definitions of AI, goals of AI like creating expert systems and implementing human intelligence in machines. It provides examples of AI like Siri, Alexa, Tesla. It also discusses current and future status of AI, advantages like less errors and faster decisions, disadvantages like high costs and unemployment. It summarizes AI tools like Amazon Web Services, AI-One, Deep Learning4J, Apache Mahout, Open Neural Networks Library. Finally, it discusses types of computing environments like personal, time sharing, client server, distributed, grid and cluster computing.
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soft computing manoj
- 2. Soft computing is a field of computer science which makes use of inexact solutions for problems which has no known method to compute an exact solution It uses imprecision, uncertainty, partial truth, and approximation as input
- 3. It’s aim is to exploit the tolerance for Approximation, Uncertainty, Imprecision, and Partial Truth in order to achieve close resemblance with human like decision making The main goal of soft computing is to develop intelligent machines to provide solutions to real world problems, which are not modeled, or too difficult to model mathematically.
- 4. Soft = Evolutionary + Neural + Fuzzy Computing Computing Network Logic Zadeh Rechenberg McCulloch Zadeh 1981 1960 1943 1965 Evolutionary = Genetic + Evolution + Evolutionary + Genetic Computing Programming Strategies programming Algorithms Rechenberg Koza Rechenberg Fogel Holland 1960 1992 1965 1962 1970
- 5. Hard computing Deals with precise values Accurate output is needed Useful in critical systems Soft computing Deals with assumptions Accuracy is not necessary Useful for routine,control, decison making tasks
- 6. • Fuzzy Systems •Neural Networks •Evolutionary Computation • Machine Learning •Probabilistic Reasoning
- 7. • Founded in 1940 • Artificial neural network mimics the biological neuron network in function k
- 8. An NN, in general, is a highly interconnected network of a large number of processing elements called neurons in an architecture inspired by the brain. NN Characteristics are:- Mapping Capabilities / Pattern Association Generalisation Robustness Fault Tolerance Parallel and High speed information processing
- 9. • Based on fuzzy set theory and fuzzy logic • Uses numeric ranges of sets (fuzzy sets ) to measure and represent the logical evaluations of partially accurate findings • Most applications in control and decision making • Founded by Lofti A Zadeh
- 11. )
- 13. Pattern recognition based on training data,Classification supervised by instructor. Unsupervised machine learning is also used where the machine learns from the given data by detecting patterns. Orange Apple? Instructor
- 14. Models based on human reasoning. Closer to human thinking and biologically inspired Models can be Linguistic Comprehensible Fast when computing Effective in practice.
- 15. Heavy industry Robotic arms, Humanoid robots Home appliances Washing machines, ACs, Refrigerators, cameras Automobiles Travel Speed Estimation, Sleep Warning Systems, Driver-less cars Spacecrafts Maneuvering of a Space Shuttle(FL), Optimization of Fuel- efficient Solutions for space craft
- 17. Soft Computing can be extended to include bio- informatics aspects. Fuzzy system can be applied to the construction of more advanced intelligent industrial systems. Soft computing is very effective when it’s applied to real world problems that are not able to solved by traditional hard computing. Soft computing enables industrial to be innovative due to the characteristics of soft computing: tractability, low cost and high machine intelligent quotient.