by International Institut for Applied Systems Analysis, Birkhäuser in Laxenburg, Austria, Boston .
Written in English
Includes bibliographical references.
|Statement||Alexander B. Kurzhanski, Vladimir M. Veliov, editors.|
|Series||Progress in systems and control theory ;, v. 18|
|Contributions||Kurzhanskiĭ, A. B., Veliov, Vladimir M.|
|LC Classifications||QA248 .M5123 1994|
|The Physical Object|
|Pagination||xi, 288 p. :|
|Number of Pages||288|
|LC Control Number||93049802|
This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a . Browse book content. About the book. Search in this book. nonlinear and uncertain systems. In this chapter, the basic problem of modeling and control of an unknown multivariable dynamic system is studied with analysis, design, and simulations. These volumes will present a logical progression from implementation and modeling techniques. The uncertain nonlinear systems can be modeled with fuzzy differential equations (FDEs) and the solutions of these equations are applied to analyze many . The rapid development of new Information Infrastructure combined with the increased user needs in specific areas of Information Technology (mostly related to Web applications) has created the need for designing new modeling techniques more innovative and targeted on specific areas of Information Systems in order to successfully model the rapidly changed environment, along Cited by: 3.
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. Download Citation | Type-2 Fuzzy Logic: Uncertain Systems’ Modeling and Control | This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control. Abstract. As discussed in Chap. 1 it is well understood that uncertainties are unavoidable in a real control system. The uncertainty can be classified into two categories: disturbance signals and dynamic perturbations. The former includes input and output disturbance (such as a gust on an aircraft), sensor noise and actuator noise, by: 4. This book uses Ptolemy II as the basis for a broad discussion of system design, modeling, and simulation techniques for hierarchical, heterogeneous systems. But it also uses Ptolemy II to ensure that the discussions are not abstract and theoretical. All of the techniques are backed by a well designed and well tested software realization.
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