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Neural Networks And Fuzzy Systems
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Book Synopsis Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering by : Nikola K. Kasabov
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.
Book Synopsis Neural Networks and Fuzzy Systems by : Bart Kosko
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.
Book Synopsis Neural Fuzzy Systems by : Ching Tai Lin
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.
Book Synopsis Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms by : Lakhmi C. Jain
Download or read book Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms written by Lakhmi C. Jain and published by CRC Press. This book was released on 2020-01-29 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS by : S. RAJASEKARAN
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.
Book Synopsis Fundamentals of Computational Intelligence by : James M. Keller
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.
Book Synopsis Fuzzy Logic and Neural Network Handbook by : Chi-hau Chen
Download or read book Fuzzy Logic and Neural Network Handbook written by Chi-hau Chen and published by McGraw-Hill Companies. This book was released on 1996 with total page 862 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical reference that presents concise and comprehensive reports on the major activities in fuzzy logic and neural networks, with emphasis on the applications and systems of interest to computer engineers. Each of the 31 chapters focuses on the most important activity of a specific topic, and the chapters are organized into three parts: principles and algorithms; applications; and architectures and systems. The applications for fuzzy logic include home appliance design and manufacturing process; those for neural networks include radar, sonar, and speech signal processing, remote sensing, and electrical power systems. Annotation copyright by Book News, Inc., Portland, OR
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.
Book Synopsis Fuzzy Neural Intelligent Systems by : Hongxing Li
Download or read book Fuzzy Neural Intelligent Systems written by Hongxing Li and published by CRC Press. This book was released on 2018-10-03 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations. Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications: Fundamental concepts and theories for fuzzy systems and neural networks. Foundation for fuzzy neural networks and important related topics Case examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systems Suitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field.
Book Synopsis Fuzzy Engineering Expert Systems with Neural Network Applications by : Adedeji Bodunde Badiru
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.
Book Synopsis Intelligent Hybrid Systems by : Da Ruan
Download or read book Intelligent Hybrid Systems written by Da Ruan and published by Springer Science & Business Media. This book was released on 1997-09-30 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Networks and Fuzzy-logic Control on Personal Computers and Workstations by : Granino Arthur Korn
Download or read book Neural Networks and Fuzzy-logic Control on Personal Computers and Workstations written by Granino Arthur Korn and published by MIT Press (MA). This book was released on 1995 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks and Fuzzy-Logic Control introduces a simple integrated environment for programming displays and report generation. It includes the only currently available software that permits combined simulation of multiple neural networks, fuzzy-logic controllers, and dynamic systems such as robots or physiological models. The enclosed educational version of DESIRE/NEUNET differs for the full system mainly in the size of its data area and includes a compiler, two screen editors, color graphics, and many ready-to-run examples. The software lets users or instructors add their own help screens and interactive menus. The version of DESIRE/NEUNET included here is for PCs, viz. 286/287, 386/387, 486DX, Pentium, P6, SX with math coprocessor.
Book Synopsis Deep Neuro-Fuzzy Systems with Python by : Himanshu Singh
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.
Book Synopsis A First Course in Fuzzy and Neural Control by : Hung T. Nguyen
Download or read book A First Course in Fuzzy and Neural Control written by Hung T. Nguyen and published by CRC Press. This book was released on 2002-11-12 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the use of fuzzy control methods has grown nearly to the level of classical control, the true understanding of fuzzy control lags seriously behind. Moreover, most engineers are well versed in either traditional control or in fuzzy control-rarely both. Each has applications for which it is better suited, but without a good understanding of
Book Synopsis Understanding Neural Networks and Fuzzy Logic by : Stamatios V. Kartalopoulos
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
Book Synopsis Fuzzy Neural Networks for Real Time Control Applications by : Erdal Kayacan
Download or read book Fuzzy Neural Networks for Real Time Control Applications written by Erdal Kayacan and published by Butterworth-Heinemann. This book was released on 2015-10-07 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS 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 and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully. Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis Contains algorithms that are applicable to real time systems Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networks Number of case studies both in identification and control Provides MATLAB® codes for some algorithms in the book
Book Synopsis Introduction to Neuro-Fuzzy Systems by : Robert Fuller
Download or read book Introduction to Neuro-Fuzzy Systems written by Robert Fuller and published by Springer Science & Business Media. This book was released on 2000 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
