Neural Fuzzy Systems
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Introduction To Neuro Fuzzy Systems
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Author : Robert Fuller
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-01-07
Introduction To Neuro Fuzzy Systems written by Robert Fuller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-07 with Business & Economics categories.
This book contains introductory material to neuro-fuzzy systems. Its main purpose is to explain the information processing in mostly-used fuzzy inference systems, neural networks and neuro-fuzzy systems. More than 180 figures and a large number of (numerical) exercises (with solutions) have been inserted to explain the principles of fuzzy, neural and neuro-fuzzy systems. Also the mathematics applied in the models is carefully explained, and in many cases exact computational formulas have been derived for the rules in error correction learning procedures. Numerous models treated in the book will help the reader to design his own neuro-fuzzy system for his specific (managerial, industrial, financial) problem. The book can serve as a textbook for students in computer and management sciences who are interested in adaptive technologies.
Neural Fuzzy Systems
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Author : Ching Tai Lin
language : en
Publisher: Prentice Hall
Release Date : 1996
Neural Fuzzy Systems written by Ching Tai Lin and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems. It includes Matlab software, with a Neural Network Toolkit, and a Fuzzy System Toolkit.
Deep Neuro Fuzzy Systems With Python
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Author : Himanshu Singh
language : en
Publisher: Apress
Release Date : 2019-11-30
Deep Neuro Fuzzy Systems With Python written by Himanshu Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Computers categories.
Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You’ll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You’ll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you’ll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You’ll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What You’ll Learn Understand fuzzy logic, membership functions, fuzzy relations, and fuzzy inference Review neural networks, back propagation, and optimization Work with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.
Fuzzy And Neuro Fuzzy Intelligent Systems
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Author : Ernest Czogala
language : en
Publisher: Physica
Release Date : 2012-08-10
Fuzzy And Neuro Fuzzy Intelligent Systems written by Ernest Czogala and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-10 with Computers categories.
Intelligence systems. We perfonn routine tasks on a daily basis, as for example: • recognition of faces of persons (also faces not seen for many years), • identification of dangerous situations during car driving, • deciding to buy or sell stock, • reading hand-written symbols, • discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: • diagnosing diseases, • localizing faults in electronic circuits, • optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.
Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering
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Author : Nikola K. Kasabov
language : en
Publisher: Marcel Alencar
Release Date : 1996
Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering written by Nikola K. Kasabov and has been published by Marcel Alencar this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
Foundations Of Neuro Fuzzy Systems
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Author : Detlef Nauck
language : en
Publisher:
Release Date : 1997-09-19
Foundations Of Neuro Fuzzy Systems written by Detlef Nauck and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-09-19 with Computers categories.
Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.
Neural Fuzzy Control Systems With Structure And Parameter Learning
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Author : C. T. Lin
language : en
Publisher: World Scientific
Release Date : 1994
Neural Fuzzy Control Systems With Structure And Parameter Learning written by C. T. Lin and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities.In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms: a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm.Both of these learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.
Introduction To Neuro Fuzzy Systems
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Author : Robert Fuller
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-05
Introduction To Neuro Fuzzy Systems written by Robert Fuller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-05 with Computers categories.
Fuzzy sets were introduced by Zadeh (1965) as a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic pro vides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the uncertainties associ ated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for rep resentating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic and classical probablity theory do not provide an appropriate conceptual framework for dealing with the representation of com monsense knowledge, since such knowledge is by its nature both lexically imprecise and noncategorical. The developement of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of uncertainty and lexical imprecision. Some of the essential characteristics of fuzzy logic relate to the following [242]. • In fuzzy logic, exact reasoning is viewed as a limiting case of ap proximate reasoning. • In fuzzy logic, everything is a matter of degree. • In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables. • Inference is viewed as a process of propagation of elastic con straints. • Any logical system can be fuzzified. There are two main characteristics of fuzzy systems that give them better performance für specific applications.