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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest;

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Fuzzy Neural Networks for Real Time Control Applications

> Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks;

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Type 2 Fuzzy Neural Networks and Their Applications

, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students;

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Neural Network Programming with Python

are discussed in this book:- A Brief Overview of Neural Networks- Backpropagation Algorithm- Neural Networks with Numpy- Improving a Neural;

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Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms

. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and;

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

type-2 fuzzy logic and their applications; the second explores new concepts and algorithms in neural networks and fuzzy logic applied to;

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

type-2 fuzzy logic and their applications; the second explores new concepts and algorithms in neural networks and fuzzy logic applied to;

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Blind Equalization in Neural Networks

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms;

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Recent Advances of Hybrid Intelligent Systems Based on Soft Computing

some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms;

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Backpropagation

Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section;

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Backpropagation

Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section;

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Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control

Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Uncertain Rule-Based Fuzzy Logic Systems

For courses in Neural Networks and Fuzzy Systems; Fuzzy Systems/Control; Fuzzy Logic.The first book of its kind, this text explains how all;

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Fuzzy Neural Intelligent Systems

of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems;

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms

their performance. The book also presents new and advanced models and algorithms of type-2 fuzzy logic and intuitionistic fuzzy systems, which;

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Diffuse Algorithms for Neural and Neuro-Fuzzy Networks

Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new;

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Simulating Neural Networks with Mathematica

with Neural Networks Feedback and Recurrent Networks Adaptive Resonance Theory Genetic Algorithms 020156629XB04062001;

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Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis

dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this;

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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms

. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural;

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Neuro-Fuzzy Equalizers for Mobile Cellular Channels

(type-2 FAF), compensatory neuro-fuzzy filter (CNFF), and radial basis function (RBF) neural network. Neuro-Fuzzy Equalizers for;

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Neuro-Fuzzy Equalizers for Mobile Cellular Channels

(type-2 FAF), compensatory neuro-fuzzy filter (CNFF), and radial basis function (RBF) neural network. Neuro-Fuzzy Equalizers for;

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Artificial Neural Networks

language, heart disease diagnosis utilizing hybrid fuzzy wavelet neural network, and hybrid neural network architecture for online learning;

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Introduction to Neural Networks for C#, 2nd Edition

backpropagation, genetic algorithms, and simulated annealing are also introduced.;

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A Learner's Guide to Fuzzy Logic Systems, Second Edition

design and selected applications. Preliminary concepts of Neural Networks and Genetic Algorithm are added features with relevant examples and;

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Fuzzy Logic Augmentation of Neural and Optimization Algorithms Theoretical Aspe

concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory;

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