Fuzzy Rule-Based Inference
Author: Fangyi Li
Publisher: Springer Nature
Published:
Total Pages: 195
ISBN-13: 981970491X
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Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases
Author: Oscar Cordon
Publisher: World Scientific
Published: 2001-07-13
Total Pages: 489
ISBN-13: 9814494453
DOWNLOAD EBOOKIn recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas.Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms.
Mathematics of Fuzzy Sets and Fuzzy Logic
Author: Barnabas Bede
Publisher: Springer
Published: 2012-12-14
Total Pages: 281
ISBN-13: 3642352219
DOWNLOAD EBOOKThis book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic. Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage. The main purpose of the present book is to reduce this gap by providing a theoretical introduction into Fuzzy Sets based on Mathematical Analysis and Approximation Theory. Well-known applications, as for example fuzzy control, are also discussed in this book and placed on new ground, a theoretical foundation. Moreover, a few advanced chapters and several new results are included. These comprise, among others, a new systematic and constructive approach for fuzzy inference systems of Mamdani and Takagi-Sugeno types, that investigates their approximation capability by providing new error estimates.
Fuzzy Systems Engineering
Author: Nadia Nedjah
Publisher: Springer Science & Business Media
Published: 2005-05-20
Total Pages: 252
ISBN-13: 9783540253228
DOWNLOAD EBOOKThis book is devoted to reporting innovative and significant progress in fuzzy system engineering. Given the maturation of fuzzy logic, this book is dedicated to exploring the recent breakthroughs in fuzziness and soft computing in favour of intelligent system engineering. This monograph presents novel developments of the fuzzy theory as well as interesting applications of the fuzzy logic exploiting the theory to engineer intelligent systems.
Fuzzy Logic Based Power-Efficient Real-Time Multi-Core System
Author: Jameel Ahmed
Publisher: Springer
Published: 2016-11-15
Total Pages: 69
ISBN-13: 9811031207
DOWNLOAD EBOOKThis book focuses on identifying the performance challenges involved in computer architectures, optimal configuration settings and analysing their impact on the performance of multi-core architectures. Proposing a power and throughput-aware fuzzy-logic-based reconfiguration for Multi-Processor Systems on Chip (MPSoCs) in both simulation and real-time environments, it is divided into two major parts. The first part deals with the simulation-based power and throughput-aware fuzzy logic reconfiguration for multi-core architectures, presenting the results of a detailed analysis on the factors impacting the power consumption and performance of MPSoCs. In turn, the second part highlights the real-time implementation of fuzzy-logic-based power-efficient reconfigurable multi-core architectures for Intel and Leone3 processors.
Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications
Author: Okyay Kaynak
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 552
ISBN-13: 3642589308
DOWNLOAD EBOOKSoft computing is a consortium of computing methodologies that provide a foundation for the conception, design, and deployment of intelligent systems and aims to formalize the human ability to make rational decisions in an environment of uncertainty and imprecision. This book is based on a NATO Advanced Study Institute held in 1996 on soft computing and its applications. The distinguished contributors consider the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications. Two areas emphasized in the book are how to achieve a synergistic combination of the main constituents of soft computing and how the combination can be used to achieve a high Machine Intelligence Quotient.
Fuzzy Inference System - Theory and Applications
Author: Mariusz Niemiec
Publisher:
Published: 2016-04-01
Total Pages: 310
ISBN-13: 9781681174808
DOWNLOAD EBOOKFuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. There are two types of fuzzy inference systems that can be implemented in the Fuzzy Logic Toolbox: Mamdani-type and Sugeno-type. These two types of inference systems vary somewhat in the way outputs are determined. Fuzzy inference systems have been successfully applied in fields such as automatic control, data classification, decision analysis, expert systems, and computer vision. Because of its multidisciplinary nature, fuzzy inference systems are associated with a number of names, such as fuzzy-rule-based systems, fuzzy expert systems, fuzzy modeling, fuzzy associative memory, fuzzy logic controllers, and simply (and ambiguously) fuzzy systems. Fuzzy Inference System - Theory and Applications attempt to accumulate the researches on diverse inter disciplinary field of engineering and management using Fuzzy Inference System (FIS). This book deals with FIS applications to management related problems, mechanical and industrial engineering problems, image processing and cognition problems, civil engineering problems, etc.
Fuzzy Rule-Based Expert Systems and Genetic Machine Learning
Author: Andreas Geyer-Schulz
Publisher: Physica
Published: 1997
Total Pages: 460
ISBN-13:
DOWNLOAD EBOOKThis book integrates fuzzy rule-languages with genetic algorithms, genetic programming, and classifier systems with the goal of obtaining fuzzy rule-based expert systems with learning capabilities. The main topics are first introduced by solving small problems, then a prototype implementation of the algorithm is explained, and last but not least the theoretical foundations are given. The second edition takes into account the rapid progress in the application of fuzzy genetic algorithms with a survey of recent developments in the field. The chapter on genetic programming has been revised. An exact uniform initialization algorithm replaces the heuristic presented in the first edition. A new method of abstraction, compound derivations, is introduced.
Neural Network-based Fuzzy Inference System
Author: Jing Lu
Publisher:
Published: 2013
Total Pages: 55
ISBN-13:
DOWNLOAD EBOOKThis paper proposes a neural network-based fuzzy inference system. The main innovation of the system is to use a neural network to express relations among fuzzy sets. To begin, we show how to represent a relation among fuzzy sets compactly using a neural network structure. We then demonstrate that it is possible to successfully train and utilize the fuzzy network with only a partial description of a desired relation among fuzzy sets. Finally, we extend our algorithms to infer fuzzy rules based on the trained fuzzy rule-base neural networks and show several examples of fuzzy inference models made using our system.
Fuzzy Logic
Author: John Yen
Publisher: Pearson
Published: 1999
Total Pages: 586
ISBN-13:
DOWNLOAD EBOOKProviding equal emphasis on theoretical foundations and practical issues, this book features fuzzy logic concepts and techniques in intelligent systems, control, and information technology. Uses Fuzzy Logic Toolbox MATLAB to demonstrate exemplar applications and to develop hands-on exercises.
