Neuro-fuzzy systems
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Neuro-fuzzy systems combine neural networks and fuzzy logic to overcome the limitations of each. They were created to achieve the mapping precision of neural networks and the interpretability of fuzzy systems. There are different types of neuro-fuzzy systems depending on whether the inputs, outputs, and weights are crisp or fuzzy. Two common models are fuzzy systems providing input to neural networks, and neural networks providing input to fuzzy systems. Neuro-fuzzy systems have applications in domains like measuring water opacity, improving financial ratings, and automatically adjusting devices.
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- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have various applications including voice recognition, image recognition, and robotics due to their ability to learn from large amounts of data.
Neural Networks and Deep Learning Basics
This document provides an introduction to deep learning and neural networks. It discusses:
- Deep learning learns representations of data rather than relying on hand-engineered features.
- Deep learning architectures include neural networks, convolutional neural networks, and recurrent neural networks.
- Deep learning represents concepts in a nested hierarchy from simple to more abstract, with each layer learning slightly more complex representations. This allows it to learn its own feature detectors from raw data.
33.-Multi-Layer-Perceptron.pdf
This document provides an overview of multi-layer perceptrons (MLPs), also known as neural networks. It begins by discussing how perceptrons work, including taking inputs, multiplying them by weights, passing them through an activation function, and producing an output. MLPs consist of multiple stacked perceptron layers that allow them to solve more complex problems. Key aspects that enable deep learning with MLPs include backpropagation to optimize weights, tuning hyperparameters like the number of layers and activation functions, and using advanced training techniques involving learning rates, epochs, batches and optimizer algorithms.
Neural network
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each link has a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell : • DENDRITES: Accepts the inputs • SOMA : Process the inputs • AXON : Turns the processed inputs into outputs. • SYNAPSES : The electrochemical contact between the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network are represented by the x1 mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): input signal. Weights (Dendrites): determines the importance of incoming value. Output (Axon): output to other neuron or of NN .
Artificial Neural Network in Medical Diagnosis
This document provides an overview of artificial neural networks. It begins with definitions of artificial neural networks and how they are analogous to biological neural networks. It then discusses the basic structure of artificial neural networks, including different types of networks like feedforward, recurrent, and convolutional networks. Key concepts in artificial neural networks like neurons, weights, forward/backward propagation, and overfitting/underfitting are also explained. The document concludes with limitations of neural networks and references.
neuralnetwork.pptx
Neural networks are programs that mimic the human brain by learning from large amounts of data. They use simulated neurons that are connected together to form networks, similar to the human nervous system. Neural networks learn by adjusting the strengths of connections between neurons, and can be used to perform tasks like pattern recognition or prediction. Common neural network training algorithms include gradient descent and backpropagation, which help minimize errors by adjusting connection weights.
Neural Networks-1
This document provides an overview of three types of machine learning: supervised learning, reinforcement learning, and unsupervised learning. It then discusses supervised learning in more detail, explaining that each training case consists of an input and target output. Regression aims to predict a real number output, while classification predicts a class label. The learning process typically involves choosing a model and adjusting its parameters to reduce the discrepancy between the model's predicted output and the true target output on each training case.
Presentationnnnn
This document provides an overview of neural networks, including their basic components and types. It discusses neurons, activation functions, and different neural network architectures like feedforward and feedback networks. It also covers supervised, unsupervised, and reinforcement learning methods. Applications of neural networks are described along with their advantages in learning from experience and dealing with incomplete information, and disadvantages like long training times.
From neural networks to deep learning
The document discusses the history and development of artificial neural networks and deep learning. It describes early neural network models like perceptrons from the 1950s and their use of weighted sums and activation functions. It then explains how additional developments led to modern deep learning architectures like convolutional neural networks and recurrent neural networks, which use techniques such as hidden layers, backpropagation, and word embeddings to learn from large datasets.
Artificial Neural Network
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
Sequence Modelling with Deep Learning
Much of data is sequential – think speech, text, DNA, stock prices, financial transactions and customer action histories. Modern methods for modelling sequence data are often deep learning-based, composed of either recurrent neural networks (RNNs) or attention-based Transformers. A tremendous amount of research progress has recently been made in sequence modelling, particularly in the application to NLP problems. However, the inner workings of these sequence models can be difficult to dissect and intuitively understand.
This presentation/tutorial will start from the basics and gradually build upon concepts in order to impart an understanding of the inner mechanics of sequence models – why do we need specific architectures for sequences at all, when you could use standard feed-forward networks? How do RNNs actually handle sequential information, and why do LSTM units help longer-term remembering of information? How can Transformers do such a good job at modelling sequences without any recurrence or convolutions?
In the practical portion of this tutorial, attendees will learn how to build their own LSTM-based language model in Keras. A few other use cases of deep learning-based sequence modelling will be discussed – including sentiment analysis (prediction of the emotional valence of a piece of text) and machine translation (automatic translation between different languages).
The goals of this presentation are to provide an overview of popular sequence-based problems, impart an intuition for how the most commonly-used sequence models work under the hood, and show that quite similar architectures are used to solve sequence-based problems across many domains.
Deep Learning Frameworks slides
The document discusses deep learning concepts and frameworks. It provides an overview of deep learning concepts such as neural networks, layers, nodes, weights, activation functions, and optimization techniques. It also discusses specific deep learning frameworks including TensorFlow, Torch, and Theano. These frameworks can be compared based on factors like speed, ease of use, programming languages, hardware support, community size, and algorithms supported.
Deep learning frameworks v0.40
The document discusses deep learning concepts and frameworks. It provides an overview of deep learning concepts such as neural networks, layers, nodes, weights, activation functions, and optimization techniques. It also discusses specific deep learning frameworks including TensorFlow, Torch, and Theano. These frameworks can be compared based on factors like speed, ease of use, programming languages, hardware support, community size, and algorithms supported.
ML Module 3 Non Linear Learning.pptx
This document provides an overview of non-linear machine learning models. It introduces non-linear models and compares them to linear models. It discusses stochastic gradient descent and batch gradient descent optimization algorithms. It also covers neural networks, including model representations, activation functions, perceptrons, multi-layer perceptrons, and backpropagation. Additionally, it discusses regularization techniques to reduce overfitting, support vector machines, and K-nearest neighbors algorithms.
shenktalk.pdf
The document discusses research on improving the performance and availability of wide-area networks. It proposes an algorithm to make network configurations highly available, collaborative, and adaptable. The algorithm is evaluated through a measurement study and is shown to prevent location-identity splits better than previous approaches. In conclusion, the presented technique called Scherzo is argued to address many challenges faced by software engineering.
Neural networks of artificial intelligence
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
Neural networks
This document provides an overview of neural networks, including their history, components, connection types, learning methods, applications, and comparison to conventional computers. It discusses how biological neurons inspired the development of artificial neurons and neural networks. The key components of biological and artificial neurons are described. Connection types in neural networks include static feedforward and dynamic feedbackward connections. Learning methods include supervised, unsupervised, and reinforcement learning. Applications span mobile computing, forecasting, character recognition, and more. Neural networks learn by example rather than requiring explicitly programmed algorithms.
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Neuro-fuzzy systems
- 1. Neuro-Fuzzy Systems (NFS) Presented by Sagar Ahire
- 2. Neuro-Fuzzy System = Neural Network + Fuzzy System
- 3. Fuzzy Logic • A form of logic that deals with approximate reasoning • Created to model human reasoning processes • Uses variables with truth values between 0 and 1
- 4. Characteristics of Fuzzy Logic • Everything is a matter of degree • Knowledge is interpreted as a collection of fuzzy constraints on a collection of variables • Inference is viewed as the process of propagation of these constraints • Any logic system can be fuzzified
- 5. Neural Network • Simplified Mathematical model of brain-like systems • Functions like a massively parallel distributed computation network • Is not programmed, but is trained
- 6. Neural Network • Input • Weights • Output
- 7. Comparison Point Fuzzy Systems Neural Network Knowledge Source Human Experts Sample Sets Learning Mechanism Induction Adjusting Weights Reasoning Mechanism Heuristic Search Parallel Computation Learning Speed High Low Reasoning Speed Low High Fault Tolerance Low Very High Implementation Explicit Implicit Flexibility Low High
- 8. Neuro-Fuzzy Systems (NFS) • Were created to solve the trade-off between: – The mapping precision & automation of Neural Networks – The interpretability of Fuzzy Systems • Combines both such that either: – Fuzzy system gives input to Neural Network – Neural Network gives input to Fuzzy Systems
- 9. Steps in Development of NFS • Development of Fuzzy Neural Models [Neurons] • Development of synaptic connection models which incorporate fuzziness into Neural Network [Weights] • Development of Learning Algorithms [Method of adjusting weights]
- 10. Types of NFS Type Weights Inputs Outputs Applications Type 0 Crisp Crisp Crisp N/A Type 1 Crisp Fuzzy Crisp Classification Type 2 Crisp Fuzzy Fuzzy Fuzzy IF-THEN Type 3 Fuzzy Fuzzy Fuzzy Fuzzy IF-THEN Type 4 Fuzzy Crisp Fuzzy Fuzzy IF-THEN Type 5 Crisp Crisp Fuzzy Unrealistic Type 6 Fuzzy Crisp Crisp Unrealistic Type 7 Fuzzy Fuzzy Crisp Unrealistic
- 11. Models of NFS • Model 1: Fuzzy System → Neural Network • Model 2: Neural Network → Fuzzy Systems
- 12. Models of NFS #1: Fuzzy System → Neural Network
- 13. Models of NFS #2: Neural Network → Fuzzy System
- 14. Applications of NFS • Measuring opacity/transparency of water in washing machine – Hitachi, Japan • Improving the rating of convertible bonds – Nikko Securities, Japan • Adjusting exposure in photocopy machines – Sanyo, Japan • Electric fan that rotates towards the user – Sanyo, Japan
Editor's Notes
- - Automatically extract fuzzy rules from numerical data