Neural Networks And Fuzzy Systems

Author : Nikola K. Kasabov
Publisher : Marcel Alencar
ISBN 13 : 0262112124
Total Pages : 581 pages
Book Rating : 4.23/5 ( download)

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Download or read book Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering written by Nikola K. Kasabov and published by Marcel Alencar. This book was released on 1996 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Bart Kosko
Publisher :
ISBN 13 :
Total Pages : 488 pages
Book Rating : 4.85/5 ( download)

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Download or read book Neural Networks and Fuzzy Systems written by Bart Kosko and published by . This book was released on 1992 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the foremost experts in the field of neural networks, this is the first book to combine the theories and applications or neural networks and fuzzy systems. The book is divided into three sections: Neural Network Theory, Neural Network Applications, and Fuzzy Theory and Applications. It describes how neural networks can be used in applications such as: signal and image processing, function estimation, robotics and control, analog VLSI and optical hardware design; and concludes with a presentation of the new geometric theory of fuzzy sets, systems, and associative memories.

Author : Ching Tai Lin
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 824 pages
Book Rating : 4.33/5 ( download)

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Download or read book Neural Fuzzy Systems written by Ching Tai Lin and published by Prentice Hall. This book was released on 1996 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : S. RAJASEKARAN
Publisher : PHI Learning Pvt. Ltd.
ISBN 13 : 812035334X
Total Pages : 576 pages
Book Rating : 4.43/5 ( download)

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Download or read book NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS written by S. RAJASEKARAN and published by PHI Learning Pvt. Ltd.. This book was released on 2017-05-01 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Stamatios V. Kartalopoulos
Publisher : Wiley-IEEE Press
ISBN 13 :
Total Pages : 240 pages
Book Rating : 4.58/5 ( download)

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Download or read book Understanding Neural Networks and Fuzzy Logic written by Stamatios V. Kartalopoulos and published by Wiley-IEEE Press. This book was released on 1996 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background. Sponsored by: IEEE Neural Networks Council

Author : James M. Keller
Publisher : John Wiley & Sons
ISBN 13 : 111921436X
Total Pages : 378 pages
Book Rating : 4.66/5 ( download)

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Download or read book Fundamentals of Computational Intelligence written by James M. Keller and published by John Wiley & Sons. This book was released on 2016-07-13 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

Author : Adedeji Bodunde Badiru
Publisher : John Wiley & Sons
ISBN 13 : 0471275344
Total Pages : 313 pages
Book Rating : 4.43/5 ( download)

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Download or read book Fuzzy Engineering Expert Systems with Neural Network Applications written by Adedeji Bodunde Badiru and published by John Wiley & Sons. This book was released on 2002-10-08 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an up-to-date integration of expert systems with fuzzy logic and neural networks. Includes coverage of simulation models not present in other books. Presents cases and examples taken from the authors' experience in research and applying the technology to real-world situations.

Author : Ronald R. Yager
Publisher : Van Nostrand Reinhold Company
ISBN 13 :
Total Pages : 456 pages
Book Rating : 4.19/5 ( download)

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Download or read book Fuzzy Sets, Neural Networks, and Soft Computing written by Ronald R. Yager and published by Van Nostrand Reinhold Company. This book was released on 1994 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brings together chapters by experts involved in a new area based on the confluence of genetic algorithms, fuzzy systems, and neural networks. Papers cover the broad ground of fuzzy logic control, neural fuzzy systems, genetic fuzzy systems, process control, and adaptive systems. Topics include the composition of heterogeneous control laws, ellipsoidal learning and fuzzy throttle control for platoons of smart cars, supervised and unsupervised learning, and propagation and satisfaction of flexible constraints. Annotation copyright by Book News, Inc., Portland, OR

Author : Hojjat Adeli
Publisher :
ISBN 13 :
Total Pages : 232 pages
Book Rating : 4.10/5 ( download)

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Download or read book Machine Learning written by Hojjat Adeli and published by . This book was released on 1995 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the only book to apply neural nets, genetic algorithms, and fuzzy set theory to the fast growing field of machine learning. Placing particular emphasis on neural networks, it explores how to integrate them with other technologies to improve their performance. Examples are included for each system discussed.

Author : Himanshu Singh
Publisher : Apress
ISBN 13 : 1484253612
Total Pages : 270 pages
Book Rating : 4.18/5 ( download)

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Download or read book Deep Neuro-Fuzzy Systems with Python written by Himanshu Singh and published by Apress. This book was released on 2019-11-30 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 inferenceReview neural networks, back propagation, and optimizationWork 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.