An original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling.�Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and�Z-Number�presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations. The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent control design and applications. The text assembles the results of four years of research on control of uncertain nonlinear systems with dual fuzzy equations, fuzzy modeling for uncertain nonlinear systems with fuzzy equations, the numerical solution of fuzzy equations with�Z-numbers, and the numerical solution of fuzzy differential equations with�Z-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book: Presents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equationsIncludes an overview of uncertain nonlinear systems for non-specialistsTeaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems developmentReinforces comprehension with illustrations, tables, examples, and simulations Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and�Z-Number�is suitable as a textbook for advanced students, academic and industrial researchers, and practitioners in fields of systems engineering, learning control systems, neural networks, computational intelligence, and fuzzy logic control.

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Detalles bibliogr�ficos

T�tulo: Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number
Editor: John Wiley and Sons Inc, US
Fecha de publicaci�n: 2019
Idioma: Ingl�s
ISBN 10: 111949155X
ISBN 13: 9781119491552
Encuadernaci�n: Hardback
Estado: New

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Sinopsis

An original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations

There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling. Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations.

The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent control design and applications. The text assembles the results of four years of research on control of uncertain nonlinear systems with dual fuzzy equations, fuzzy modeling for uncertain nonlinear systems with fuzzy equations, the numerical solution of fuzzy equations with Z-numbers, and the numerical solution of fuzzy differential equations with Z-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book:

Presents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations
Includes an overview of uncertain nonlinear systems for non-specialists
Teaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development
Reinforces comprehension with illustrations, tables, examples, and simulations

Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number is suitable as a textbook for advanced students, academic and industrial researchers, and practitioners in fields of systems engineering, learning control systems, neural networks, computational intelligence, and fuzzy logic control.

Acerca del autor

Wen Yu, PhD, is Professor at CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico. He is Associate Editor of IEEE Transactions on Cybernetics, Neurocomputing, and Journal of Intelligent and Fuzzy Systems. Dr. Yu is a member of the Mexican Academy of Sciences.

Raheleh Jafari is a postdoctoral research fellow at Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway. She is on the editorial board of the Journal of Intelligent and Fuzzy Systems, and served as a reviewer in various journals and conferences. Her research interest is in the field of artificial intelligence, fuzzy control, machine learning, nonlinear systems, neural networks, and fuzzy engineering.

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