Fuzzy if-then rules in computational intelligence

theory and applications

Publisher: Kluwer Academic in Boston

Written in English
Published: Pages: 322 Downloads: 425
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Subjects:

  • Expert systems (Computer science),
  • Fuzzy systems,
  • Computational intelligence

Edition Notes

Includes bibliographical references and index.

Statementedited by Da Ruan, Etienne E. Kerre.
SeriesThe Kluwer international series in engineering and computer science -- SECS 553, The Kluwer international series in engineering and computer science -- SECS 553, Kluwer international series in engineering and computer science -- SECS 553....
ContributionsRuan, Da., Kerre, Etienne E.
Classifications
LC ClassificationsQA76.76.E95 F8825 2000
The Physical Object
Paginationx, 322 p. :
Number of Pages322
ID Numbers
Open LibraryOL18293078M
ISBN 100792378202
LC Control Number00022047

Erdal Kayacan, Mojtaba Ahmadieh Khanesar, in Fuzzy Neural Networks for Real Time Control Applications, Defuzzification. The final step is a defuzzification process where the fuzzy output is translated into a single crisp value, like the fuzzification process, by the degree of membership values. Defuzzification is an inverse transformation compared with the fuzzification process. Book Title. Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. Additional Information. How to Cite. Siddique, N. and Adeli, H. () Neural Networks, in Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, John Wiley & Sons Ltd, Oxford, UK. doi: This refreshing view has set the book apart from other books on computational intelligence. The book has an emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field.   This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational.

Witold Pedrycz is a Professor and Canada Research Chair (CRC—Computational Intelligence) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He also holds an appointment of special professorship in the School of Computer Science, University of Cited by: Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Ebrahim (Abe) H. Mamdani (June 1, – Janu ) was a mathematician, computer scientist, electrical engineer and artificial intelligence researcher. He worked at the Imperial College London.. Life. Abe Mamdani was born in Tanzania in June He was educated in India and in he went to : IEEE Fellow, IFSA Fellow, RAEng Fellow, .

Computational intelligence is a rapidly growing research field including a wide variety of problem-solving techniques inspired by nature. Traditionally computational intelligence consists of three major research areas: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Neural networks are mathematical models inspired by brains. The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.. Generally, computational intelligence is a set of nature-inspired computational methodologies.

Fuzzy if-then rules in computational intelligence Download PDF EPUB FB2

The main body of this book consists of so-called IF-THEN rules, on which experts express their knowledge with respect to a certain domain of expertise. Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and processing of IF-THEN by: The main body of this book consists of so-called IF-THEN rules, on which experts express their knowledge with respect to a certain domain of expertise.

Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and processing of IF-THEN cturer: Springer.

The main body of this book consists of so-called IF-THEN rules, on which experts express their knowledge with respect to a certain domain of expertise. Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and processing of IF-THEN : Hardcover.

[(Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications)] [Author: Da Ruan] [Apr] on *FREE* shipping on qualifying offers. [(Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications)] [Author: Da Ruan] [Apr]Manufacturer: Springer-Verlag New York Inc.

Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and Fuzzy if-then rules in computational intelligence book of IF-THEN rules.

This work gives special attention to fuzzy IF-THEN rules as they are being applied in computational intelligence.

The main body of this book consists of so-called IF-THEN rules, on which experts express their knowledge with respect to a certain domain of expertise. Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and processing of IF-THEN rules.

"Fuzzy IF-THEN Rules in Computational Intelligence: Theory and Applications brings together contributions from leading global specialists who work in the domain of representation and processing of IF-THEN rules.

This work gives special attention to fuzzy IF-THEN rules as they are being applied in computational intelligence. A fuzzy if-then rule is generated in each fuzzy subspace. Using the heuristic rule generation method, we examine some basic aspects of Fuzzy if-then rules in computational intelligence book rule-based classification systems such as the shape of membership functions, the definition of the compatibility grade, and the choice of a fuzzy reasoning Cited by: 6.

For the advanced applications, for example in robotics or artificial intelligence, however, it is a challenge to take fuzzy IF-THEN rules as genuine expressions of natural language and to capture their meaning from this point of by: Fuzzy IF-THEN rules from logical point of view.

The theory of IF-THEN rules proposed by Lotfi A. Zadeh attracted many researchers and practitioners because of its simplicity and elegance. This contribution is an attempt to create a comprehensive logical theory of fuzzy IF-THEN rules based on Hajek's predicate BL-fuzzy logic. Abstract: This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power.

The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number Cited by: from book Fuzzy if-then rules in computational intelligence.

generating fuzzy if-then rules for pattern classification problems from training patterns. fuzzy rules for pattern. Neural networks that learn from fuzzy if-then rules Abstract: An architecture for neural networks that can handle fuzzy input vectors is proposed, and learning algorithms that utilize fuzzy if-then rules as well as numerical data in neural network learning for classification problems and for fuzzy Cited by: Then we propose a fuzzy if-then rules extraction-based LNHD fusion method.

It can construct a uniform fuzzy rule base through the fusion of the fuzzy rule bases obtained from Cited by: 3.

The result is a holistic view of fuzzy sets as a fundamental component of computational intelligence and human-centric systems. Throughout the book, the authors emphasize the direct applicability and limitations of the concepts being discussed, and historical and bibliographical notes are included in each chapter to help readers view the.

Computational Intelligence Website to the Corresponding Book. Fuzzy Systems. Lecture Slides. Introduction, fuzzy sets and fuzzy logic. The book offers the first comprehensive guide on interval-valued intuitionistic fuzzy sets.

By providing the readers with a thorough survey and important practical details, it is expected to support them in carrying out applied research and to encourage them to test the. Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence.

Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an Reviews: 1. This book constitutes the refereed proceedings of the 8th Dortmund Fuzzy Days, held in Dortmund, Germany, The Fuzzy-Days conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence.

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. Traditionally computational intelligence consists of three major research areas: Neural Networks, Fuzzy Systems, and Evolutionary Computation.

Neural networks are mathematical models inspired by brains. Neural networks have massively parallel network structures with many neurons and weighted connections. Fuzzy inference system.

Fuzzy inference is a method that interprets the values in the input vector and, based on some sets of rules, assigns values to the output vector. In fuzzy logic, the truth of any statement becomes a matter of a degree. Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic.

Ebook Theory of Fuzzy Computation Full Download. LianneCostello. Follow. 4 Download Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications (The Springer. Elissapetropoulos. PDF Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications (The Springer [Read Book] Fuzzy and Neuro-Fuzzy.

This book is intended as an introduction to fuzzy algebraic hyperstructures. As the first in its genre, it includes a number of topics, most of which reflect the authors’ past research and thus provides a starting point for future research directions.

The book is organized in five chapters. The. Fuzzy logic systems address the imprecision of the input and output variables directly by defining them with fuzzy numbers (and fuzzy sets) that can be expressed in linguistic terms (e.g., small, medium and large).

The basic configuration of the T–S system includes a fuzzy rule base, which consists of a collection of fuzzy IF–THEN rules in the following form (Wang, ; Zelinka and.

Smooth Extensions of Fuzzy If-Then Rule Bases Thomas Vetterlein. Pre-validation of a Fuzzy Model Farida Benmakrouha. Multiresolution Fuzzy Rule Systems Ricardo Ñanculef, Carlos Concha, Claudio Moraga, Héctor Allende. Fuzzy Clustering of Macroarray Data Olga Georgieva, Frank Klawonn, Elizabeth Härtig.

Edition: 1. 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. Book Title. Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing Siddique, N. and Adeli, H. () Neural Fuzzy Systems, in Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, John Wiley & Sons Ltd, Oxford, UK.

doi: /ch This book constitutes the refereed proceedings of the 9th Dortmund Fuzzy Days, held in Dortmund, Germany, The Fuzzy Days conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. All the papers had to undergo a.

Ten years of,Fuzzy Days“ in Dortmund. What started as a relatively small workshop in has now become one of the best known smaller conferences on Computational Intelligence in the world. It fact. Computational Intelligence and Feature Selection provides readers with the background and fundamental ideas behind Feature Selection (FS), with an emphasis on techniques based on rough and fuzzy sets.

For readers who are less familiar with the subject, the book begins with an introduction to fuzzy set theory and fuzzy-rough set by: This book constitutes the refereed proceedings of the International Conference on Computational Intelligence held in Dortmund, Germany, as the 5th Fuzzy Days, in April Besides three invited contributions, the book presents 53 revised full papers selected from a total of submissions.

Also.Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments.

The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial.