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 2013-06-05 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Download or read book Fuzzy and Neuro Fuzzy Systems in Medicine written by Horia-Nicolai L Teodorescu and published by CRC Press. This book was released on 2017-11-22 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy and Neuro-Fuzzy Systems in Medicineprovides a thorough review of state-of-the-art techniques and practices, defines and explains relevant problems, as well as provides solutions to these problems. After an introduction, the book progresses from one topic to another - with a linear development from fundamentals to applications.

Download or read book Neuro fuzzy and Soft Computing written by Jyh-Shing Roger Jang and published by Pearson Education. This book was released on 1997 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, and several stochastic optimization methods that do not require gradient information. In particular, the authors put equal emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than 150 exercises, over 300 illustrations, and more than 150 Matlab scripts. In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization (such as genetic algorithms, simulated annealing, random search, and downhill Simplex method). The presentation also makes use of SIMULINK for neuro-fuzzy control system simulations. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an "Internet Resource Page" to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc. All the HTTP and FTP addresses are available as a bookmark file on the companion software disk.

Download or read book Fuzzy and Neuro Fuzzy Intelligent Systems written by Ernest Czogała and published by Springer Science & Business Media. This book was released on 2000-04-06 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides an introduction to basic concepts as well as some recent advancements in fuzzy set theory, approximate reasoning, artificial neural networks and clustering methods. These methodologies create together the so-called soft computing, which is part of a computational approach to system intelligence. The book deals with an overview of fuzzy set theory, foundations for approximate reasoning principles, specific equivalence of inference results using logical conjunctive interpretations of if-then rules, supervised and unsupervised artificial neural networks, a new generalized conditional fuzzy clustering method, artificial neural networks-based fuzzy inference system with parameterized consequences in if-then rules, MATLAB(R) m-files implementation of neuro-fuzzy systems, detailed study of neuro-fuzzy systems applications.

Download or read book Foundations of Neuro Fuzzy Systems written by Detlef Nauck and published by . This book was released on 1997-09-19 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Fuzzy and Neuro Fuzzy Systems in Medicine written by Horia-Nicolai L Teodorescu and published by CRC Press. This book was released on 2017-11-22 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fuzzy and Neuro-Fuzzy Systems in Medicineprovides a thorough review of state-of-the-art techniques and practices, defines and explains relevant problems, as well as provides solutions to these problems. After an introduction, the book progresses from one topic to another - with a linear development from fundamentals to applications.

Download or read book Neural Fuzzy Control Systems With Structure And Parameter Learning written by Chin-teng Lin and published by World Scientific Publishing Company. This book was released on 1994-02-08 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Download or read book Flexible Neuro Fuzzy Systems written by Leszek Rutkowski and published by Springer Science & Business Media. This book was released on 2004-05-19 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flexible Neuro-Fuzzy Systems is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data. Flexible Neuro-Fuzzy Systems is the only book that proposes a flexible approach to fuzzy modeling and fills the gap in existing literature. This book introduces new fuzzy systems which outperform previous approaches to system modeling and classification, and has the following features: -Provides a framework for unification, construction and development of neuro-fuzzy systems; -Presents complete algorithms in a systematic and structured fashion, facilitating understanding and implementation, -Covers not only advanced topics but also fundamentals of fuzzy sets, -Includes problems and exercises following each chapter, -Illustrates the results on a wide variety of simulations, -Provides tools for possible applications in business and economics, medicine and bioengineering, automatic control, robotics and civil engineering.

Download or read book Introduction to Applied Fuzzy Electronics written by Ahmad M. Ibrahim and published by . This book was released on 1997 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained, compact introduction to fuzzy logic from an applied electronics point of view. It presents fuzzy electronics as a generalization of digital electronics with the goal of making fuzzy logic easily accessible to practicing engineers and students alike.

Download or read book Introduction to Fuzzy Systems written by Guanrong Chen and published by CRC Press. This book was released on 2005-11-16 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subjec

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 574 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.

Download or read book Computational Intelligence written by Nazmul Siddique and published by John Wiley & Sons. This book was released on 2013-05-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book An Introduction to Fuzzy Logic and Fuzzy Sets written by James J. Buckley and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an excellent starting point for any curriculum in fuzzy systems fields such as computer science, mathematics, business/economics and engineering. It covers the basics leading to: fuzzy clustering, fuzzy pattern recognition, fuzzy database, fuzzy image processing, soft computing, fuzzy applications in operations research, fuzzy decision making, fuzzy rule based systems, fuzzy systems modeling, fuzzy mathematics. It is not a book designed for researchers - it is where you really learn the "basics" needed for any of the above-mentioned applications. It includes many figures and problem sets at the end of sections.

Download or read book Flexible Neuro Fuzzy Systems written by Leszek Rutkowski and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Flexible Neuro-Fuzzy Systems is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data. Flexible Neuro-Fuzzy Systems is the only book that proposes a flexible approach to fuzzy modeling and fills the gap in existing literature. This book introduces new fuzzy systems which outperform previous approaches to system modeling and classification, and has the following features: -Provides a framework for unification, construction and development of neuro-fuzzy systems; -Presents complete algorithms in a systematic and structured fashion, facilitating understanding and implementation, -Covers not only advanced topics but also fundamentals of fuzzy sets, -Includes problems and exercises following each chapter, -Illustrates the results on a wide variety of simulations, -Provides tools for possible applications in business and economics, medicine and bioengineering, automatic control, robotics and civil engineering.

Download or read book Uncertain Rule Based Fuzzy Systems written by Jerry M. Mendel and published by Springer. This book was released on 2017-05-17 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.