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This book includes a set of selected revised and extended versions of the best papers presented at the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) – held as an online event, from October 25 to 27, 2021. We focus on three outstanding fields of Computational Intelligence through the selected panel, namely: Evolutionary Computation, Fuzzy Computation, and Neural Computation. Besides presenting the recent advances of the selected areas, the book aims to aggregate new and innovative solutions for confirmed researchers and on the other hand to provide a source of information and/or inspiration for young interested researchers or learners in the ever-expanding and current field of Computational Intelligence. It constitutes a precious provision of knowledge for individual researchers as well as represent a valuable sustenance for collective use in academic libraries (of universities and engineering schools) relating innovative techniques in various fieldsof applications.
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Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques
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The book presents on a research level the most recent and advanced research results, promotes the research of polynomial-fuzzy-model-based control systems, and provides theoretical support and point a research direction to postgraduate students and fellow researchers. Each chapter provides numerical examples to verify the analysis results, demonstrate the effectiveness of the proposed polynomial fuzzy control schemes, and explain the design procedure. The book is comprehensively written enclosing detailed derivation steps and mathematical derivations also for readers without extensive knowledge on the topics including students with control background who are interested in polynomial fuzzy model-based control systems.
Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques
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Linear-Matrix-Inequality Approach
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This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for various examples, which fill certain gaps in this area of research and may serve as benchmark solutions for the readers. Interval type-2 T-S fuzzy models provide a convenient and flexible method for analysis and synthesis of complex nonlinear systems with uncertainties.
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