Differential evolution algorithm with type-2 fuzzy logic for dynamic parameter adaptation with application to intelligent control / Oscar Castillo, Patricia Ochoa, Jose Soria.
2021
QA9.64 .C27 2021eb
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Title
Differential evolution algorithm with type-2 fuzzy logic for dynamic parameter adaptation with application to intelligent control / Oscar Castillo, Patricia Ochoa, Jose Soria.
Author
ISBN
9783030621339 (electronic bk.)
3030621332 (electronic bk.)
3030621324
9783030621322
3030621332 (electronic bk.)
3030621324
9783030621322
Published
Cham : Springer, 2021.
Copyright
©2021
Language
English
Description
1 online resource (vii, 61 pages)
Item Number
10.1007/978-3-030-62133-9 doi
Call Number
QA9.64 .C27 2021eb
Dewey Decimal Classification
511.3/13
Summary
This book focuses on the fields of fuzzy logic, bio-inspired algorithm, especially the differential evolution algorithm and also considering the fuzzy control area. The main idea is that these two areas together can help solve various control problems and to find better results. In this book, the authors test the proposed method using five benchmark control problems. First, the water tank, temperature, mobile robot, and inverted pendulum controllers are considered. For these 4 problems, experimentation was carried out using a Type-1 fuzzy system and an Interval Type-2 system. The last control problem was the D.C. motor, for which the experiments were performed with Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results obtained in each of the controllers are better and with increasing uncertainty, the results are even better. For this reason, the authors consider in this book the proposed method using fuzzy systems and the differential evolution algorithm to improve the fuzzy controllers behavior in complex control problems.
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Includes bibliographical references and index.
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SpringerBriefs in applied sciences and technology. Computational intelligence.
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Table of Contents
Introduction
Fuzzy Logic Systems
Differential Evolution Algorithm
Proposed Method
Case studies
Conclusions.
Fuzzy Logic Systems
Differential Evolution Algorithm
Proposed Method
Case studies
Conclusions.