Introduction To Neural Networks Fuzzy Logic Genetic Algorithms

Author : S. RAJASEKARAN,G.A. VIJAYALAKSHMI PAI
Publisher : PHI Learning Pvt. Ltd.
Page : 576 pages
File Size : 43,7 Mb
Release : 2017-05-01
Category : Computers
ISBN : 9788120353343

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NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS by S. RAJASEKARAN,G.A. VIJAYALAKSHMI PAI Pdf

The second edition of this book provides a comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence, which in recent years, has turned synonymous to it. The constituent technologies discussed comprise neural network (NN), fuzzy system (FS), evolutionary algorithm (EA), and a number of hybrid systems, which include classes such as neuro-fuzzy, evolutionary-fuzzy, and neuro-evolutionary systems. The hybridization of the technologies is demonstrated on architectures such as fuzzy backpropagation network (NN-FS hybrid), genetic algorithm-based backpropagation network (NN-EA hybrid), simplified fuzzy ARTMAP (NN-FS hybrid), fuzzy associative memory (NN-FS hybrid), fuzzy logic controlled genetic algorithm (EA-FS hybrid) and evolutionary extreme learning machine (NN-EA hybrid) Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book, with a wealth of information that is clearly presented and illustrated by many examples and applications, is designed for use as a text for the courses in soft computing at both the senior undergraduate and first-year postgraduate levels of computer science and engineering. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.

Author : Sudarshan K. Valluru,Rao Nageswara T.
Publisher : Unknown
Page : 0 pages
File Size : 51,7 Mb
Release : 2010
Category : Fuzzy logic
ISBN : 8184950799

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Introduction to Neural Networks, Fuzzy Logic & Genetic Algorithms by Sudarshan K. Valluru,Rao Nageswara T. Pdf

Author : Nazmul Siddique,Hojjat Adeli
Publisher : John Wiley & Sons
Page : 524 pages
File Size : 47,9 Mb
Release : 2013-05-06
Category : Technology & Engineering
ISBN : 9781118534816

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Computational Intelligence by Nazmul Siddique,Hojjat Adeli Pdf

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

Author : Lakhmi C. Jain,N.M. Martin
Publisher : CRC Press
Page : 363 pages
File Size : 51,9 Mb
Release : 2020-01-29
Category : Computers
ISBN : 9781000715125

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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms by Lakhmi C. Jain,N.M. Martin Pdf

Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include: direct frequency converters electro-hydraulic systems motor control toaster control speech recognition vehicle routing fault diagnosis Asynchronous Transfer Mode (ATM) communications networks telephones for hard-of-hearing people control of gas turbine aero-engines telecommunications systems design Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Author : Da Ruan
Publisher : Springer Science & Business Media
Page : 364 pages
File Size : 55,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461561910

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Intelligent Hybrid Systems by Da Ruan Pdf

Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume. This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks and evolutionary computation. The emphasis is on development of cooperative models of hybrid systems. Included are applications related to intelligent data analysis, process analysis, intelligent adaptive information systems, systems identification, nonlinear systems, power and water system design, and many others. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms provides researchers and engineers with up-to-date coverage of new results, methodologies and applications for building intelligent systems capable of solving large-scale problems.

Author : Lakhmi C. Jain,N.M. Martin
Publisher : CRC Press
Page : 366 pages
File Size : 45,5 Mb
Release : 2020-01-29
Category : Computers
ISBN : 9781000722949

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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms by Lakhmi C. Jain,N.M. Martin Pdf

Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include: direct frequency converters electro-hydraulic systems motor control toaster control speech recognition vehicle routing fault diagnosis Asynchronous Transfer Mode (ATM) communications networks telephones for hard-of-hearing people control of gas turbine aero-engines telecommunications systems design Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Author : David W. Pearson,Nigel C. Steele,Rudolf Albrecht
Publisher : Springer Science & Business Media
Page : 274 pages
File Size : 51,6 Mb
Release : 2011-06-28
Category : Computers
ISBN : 9783709106464

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Artificial Neural Nets and Genetic Algorithms by David W. Pearson,Nigel C. Steele,Rudolf Albrecht Pdf

The 2003 edition of ICANNGA marks a milestone in this conference series, because it is the tenth year of its existence. The series began in 1993 with the inaugural conference at Innsbruck in Austria. At that first conference, the organisers decided to organise a similar scientific meeting every two years. As a result, conferences were organised at Ales in France (1995), Norwich in England (1997), Portoroz in Slovenia (1999) and Prague in the Czech Republic (2001). It is a great honour that the conference is taking place in France for the second time. Each edition of ICANNGA has been special and had its own character. Not only that, participants have been able to sample the life and local culture in five different European coun tries. Originally limited to neural networks and genetic algorithms the conference has broadened its outlook over the past ten years and now includes papers on soft computing and artificial intelligence in general. This is one of the reasons why the reader will find papers on fuzzy logic and various other topics not directly related to neural networks or genetic algorithms included in these proceedings. We have, however, kept the same name, "International Conference on Artificial Neural Networks and Genetic Algorithms". All of the papers were sorted into one of six principal categories: neural network theory, neural network applications, genetic algorithm and evolutionary computation theory, genetic algorithm and evolutionary computation applications, fuzzy and soft computing theory, fuzzy and soft computing applications.

Author : Andrea Tettamanzi,Marco Tomassini
Publisher : Springer Science & Business Media
Page : 335 pages
File Size : 48,7 Mb
Release : 2013-04-17
Category : Mathematics
ISBN : 9783662043356

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Soft Computing by Andrea Tettamanzi,Marco Tomassini Pdf

Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

Author : Yanqing Zhang,Abraham Kandel
Publisher : World Scientific
Page : 200 pages
File Size : 51,9 Mb
Release : 1998-08-22
Category : Computers
ISBN : 9789814496575

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Compensatory Genetic Fuzzy Neural Networks and Their Applications by Yanqing Zhang,Abraham Kandel Pdf

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques. Contents:Fuzzy Compensation PrinciplesNormal Fuzzy Reasoning MethodologyCompensatory Genetic Fuzzy Neural NetworksFuzzy Knowledge Rediscovery in Fuzzy Rule BasesFuzzy Cart-Pole Balancing Control SystemsFuzzy Knowledge Compression and ExpansionHighly Nonlinear System Modeling and PredictionFuzzy Moves in Fuzzy GamesGenetic Neuro-Fuzzy Pattern RecognitionConstructive Approach to Modeling Fuzzy Systems Readership: Graduate students, researchers and experts in fuzzy logic, neural networks and genetic algorithms, and their applications. Keywords:Neural Networks;Fuzzy Logic;Genetic Algorithms;Evolutionary Computation;Granular Computing;Pattern Recognition;Data Mining;Knowledge Discovery;Nonlinear System Modeling;Game Theory;Control;Uncertainty Management;Decision Making;Compensatory Genetic Fuzzy Neural Networks

Author : G. Tayfur
Publisher : WIT Press
Page : 289 pages
File Size : 50,5 Mb
Release : 2014-11-02
Category : Technology & Engineering
ISBN : 9781845646363

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Soft Computing in Water Resources Engineering by G. Tayfur Pdf

Engineers have attempted to solve water resources engineering problems with the help of empirical, regression-based and numerical models. Empirical models are not universal, nor are regression-based models. The numerical models are, on the other hand, physics-based but require substantial data measurement and parameter estimation. Hence, there is a need to employ models that are robust, user-friendly, and practical and that do not have the shortcomings of the existing methods. Artificial intelligence methods meet this need. Soft Computing in Water Resources Engineering introduces the basics of artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA). It gives details on the feed forward back propagation algorithm and also introduces neuro-fuzzy modelling to readers. Artificial intelligence method applications covered in the book include predicting and forecasting floods, predicting suspended sediment, predicting event-based flow hydrographs and sedimentographs, locating seepage path in an earth-fill dam body, and the predicting dispersion coefficient in natural channels. The author also provides an analysis comparing the artificial intelligence models and contemporary non-artificial intelligence methods (empirical, numerical, regression, etc.). The ANN, FL, and GA are fairly new methods in water resources engineering. The first publications appeared in the early 1990s and quite a few studies followed in the early 2000s. Although these methods are currently widely known in journal publications, they are still very new for many scientific readers and they are totally new for students, especially undergraduates. Numerical methods were first taught at the graduate level but are now taught at the undergraduate level. There are already a few graduate courses developed on AI methods in engineering and included in the graduate curriculum of some universities. It is expected that these courses, too, will soon be taught at the undergraduate levels.

Author : Takeshi Furuhashi
Publisher : Unknown
Page : 240 pages
File Size : 52,5 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 3662191717

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Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms by Takeshi Furuhashi Pdf

Author : R. Lowen,A. Verschoren
Publisher : Springer Science & Business Media
Page : 463 pages
File Size : 49,8 Mb
Release : 2007-10-27
Category : Mathematics
ISBN : 9781402066689

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Foundations of Generic Optimization by R. Lowen,A. Verschoren Pdf

This is a comprehensive overview of the basics of fuzzy control, which also brings together some recent research results in soft computing, in particular fuzzy logic using genetic algorithms and neural networks. This book offers researchers not only a solid background but also a snapshot of the current state of the art in this field.

Author : Dilip Kumar Pratihar
Publisher : Alpha Science International, Limited
Page : 258 pages
File Size : 48,7 Mb
Release : 2008
Category : Fuzzy logic
ISBN : UCSC:32106019498283

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Soft Computing by Dilip Kumar Pratihar Pdf

Offers an introduction to soft computing, a family consisting of many members, namely Genetic Algorithms (GAs), Fuzzy Logic (FL), Neural Networks (NNs) and others. In this book, the working cycle of a GA is explained in detail. It discusses the mechanisms of some specialized Gas with examples.

Author : Elie Sanchez,Takanori Shibata,Lotfi Asker Zadeh
Publisher : World Scientific
Page : 254 pages
File Size : 45,7 Mb
Release : 1997
Category : Computers
ISBN : 9810224230

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Genetic Algorithms and Fuzzy Logic Systems by Elie Sanchez,Takanori Shibata,Lotfi Asker Zadeh Pdf

Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce the human-centered intelligent systems of tomorrow; the collection of papers presented in this book shows the way. The book also contains an extensive bibliography on fuzzy logic and genetic algorithms.

Author : Lefteri H. Tsoukalas,Robert E. Uhrig
Publisher : Wiley-Interscience
Page : 618 pages
File Size : 46,8 Mb
Release : 1997-02-05
Category : Computers
ISBN : UOM:39015038592898

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Fuzzy And Neural Approaches in Engineering by Lefteri H. Tsoukalas,Robert E. Uhrig Pdf

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. This definitive book presents the fundamentals of both technologies, and demonstrates how to combine the unique capabilities of these two technologies for the greatest advantage. Steering clear of unnecessary mathematics, the book highlights a wide range of dynamic possibilities and offers numerous examples to illuminate key concepts. It also explores the value of relating genetic algorithms and expert systems to fuzzy and neural technologies.