Introduction To Neuro Fuzzy Systems PDF Download
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Author | : Robert Fuller |
Publisher | : Springer Science & Business Media |
Total Pages | : 300 |
Release | : 2013-06-05 |
Genre | : Computers |
ISBN | : 3790818526 |
Download Introduction to Neuro-Fuzzy Systems Book in PDF, ePub and Kindle
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.
Author | : Detlef Nauck |
Publisher | : |
Total Pages | : 328 |
Release | : 1997-09-19 |
Genre | : Computers |
ISBN | : |
Download Foundations of Neuro-Fuzzy Systems Book in PDF, ePub and Kindle
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.
Author | : Ernest Czogała |
Publisher | : Springer Science & Business Media |
Total Pages | : 220 |
Release | : 2000-04-06 |
Genre | : Business & Economics |
ISBN | : 9783790812893 |
Download Fuzzy and Neuro-Fuzzy Intelligent Systems Book in PDF, ePub and Kindle
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.
Author | : Ching Tai Lin |
Publisher | : Prentice Hall |
Total Pages | : 824 |
Release | : 1996 |
Genre | : Computers |
ISBN | : |
Download Neural Fuzzy Systems Book in PDF, ePub and Kindle
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 | : Horia-Nicolai L Teodorescu |
Publisher | : CRC Press |
Total Pages | : 428 |
Release | : 2017-11-22 |
Genre | : Medical |
ISBN | : 1351364529 |
Download Fuzzy and Neuro-Fuzzy Systems in Medicine Book in PDF, ePub and Kindle
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.
Author | : Horia-Nicolai L Teodorescu |
Publisher | : CRC Press |
Total Pages | : 500 |
Release | : 2017-11-22 |
Genre | : Medical |
ISBN | : 1351364510 |
Download Fuzzy and Neuro-Fuzzy Systems in Medicine Book in PDF, ePub and Kindle
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.
Author | : Chin-teng Lin |
Publisher | : World Scientific Publishing Company |
Total Pages | : 152 |
Release | : 1994-02-08 |
Genre | : Technology & Engineering |
ISBN | : 9813104708 |
Download Neural Fuzzy Control Systems With Structure And Parameter Learning Book in PDF, ePub and Kindle
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.
Author | : Frank L. Lewis |
Publisher | : SIAM |
Total Pages | : 252 |
Release | : 2002-01-01 |
Genre | : Technology & Engineering |
ISBN | : 0898715059 |
Download Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities Book in PDF, ePub and Kindle
Brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities.
Author | : S. RAJASEKARAN |
Publisher | : PHI Learning Pvt. Ltd. |
Total Pages | : 574 |
Release | : 2017-05-01 |
Genre | : Computers |
ISBN | : 812035334X |
Download NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS Book in PDF, ePub and Kindle
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 | : Himanshu Singh |
Publisher | : Apress |
Total Pages | : 270 |
Release | : 2019-11-30 |
Genre | : Computers |
ISBN | : 1484253612 |
Download Deep Neuro-Fuzzy Systems with Python Book in PDF, ePub and Kindle
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.