Foundations Of Neuro Fuzzy Systems
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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.
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.
Genetic Fuzzy Systems Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases
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Author : Oscar Cordon
language : en
Publisher: World Scientific
Release Date : 2001-07-13
Genetic Fuzzy Systems Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases written by Oscar Cordon and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-13 with Computers categories.
In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas.Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms.
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.
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.
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.
Neuro Fuzzy Architectures And Hybrid Learning
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Author : Danuta Rutkowska
language : en
Publisher: Physica
Release Date : 2012-11-13
Neuro Fuzzy Architectures And Hybrid Learning written by Danuta Rutkowska and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-13 with Computers categories.
The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ ence of the human mind as a role model is clearly visible in the methodolo gies which have emerged, mainly during the past two decades, for the con ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.
Foundations Of Fuzzy Control
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Author : Jan Jantzen
language : en
Publisher: John Wiley & Sons
Release Date : 2013-07-17
Foundations Of Fuzzy Control written by Jan Jantzen and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-17 with Technology & Engineering categories.
Foundations of Fuzzy Control: A Practical Approach, 2nd Edition has been significantly revised and updated, with two new chapters on Gain Scheduling Control and Neurofuzzy Modelling. It focuses on the PID (Proportional, Integral, Derivative) type controller which is the most widely used in industry and systematically analyses several fuzzy PID control systems and adaptive control mechanisms. This new edition covers the basics of fuzzy control and builds a solid foundation for the design of fuzzy controllers, by creating links to established linear and nonlinear control theory. Advanced topics are also introduced and in particular, common sense geometry is emphasised. Key features Sets out practical worked through problems, examples and case studies to illustrate each type of control system Accompanied by a website hosting downloadable MATLAB programs Accompanied by an online course on Fuzzy Control which is taught by the author. Students can access further material and enrol at the companion website Foundations of Fuzzy Control: A Practical Approach, 2nd Edition is an invaluable resource for researchers, practitioners, and students in engineering. It is especially relevant for engineers working with automatic control of mechanical, electrical, or chemical systems.
Computational Intelligence In Intelligent Data Analysis
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Author : Christian Moewes
language : en
Publisher: Springer
Release Date : 2012-08-23
Computational Intelligence In Intelligent Data Analysis written by Christian Moewes and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-23 with Computers categories.
Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intelligence has been collected in this volume.
Computational Intelligence Soft Computing And Fuzzy Neuro Integration With Applications
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Author : Okyay Kaynak
language : en
Publisher: Springer Science & Business Media
Release Date : 1998-08-20
Computational Intelligence Soft Computing And Fuzzy Neuro Integration With Applications written by Okyay Kaynak 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 1998-08-20 with Computers categories.
Soft computing is a consortium of computing methodologies that provide a foundation for the conception, design, and deployment of intelligent systems and aims to formalize the human ability to make rational decisions in an environment of uncertainty and imprecision. This book is based on a NATO Advanced Study Institute held in 1996 on soft computing and its applications. The distinguished contributors consider the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications. Two areas emphasized in the book are how to achieve a synergistic combination of the main constituents of soft computing and how the combination can be used to achieve a high Machine Intelligence Quotient.