{"product_id":"modeling-and-control-of-uncertain-nonlinear-systems-with-fuzzy-equations-and-z-number-isbn-9781119491552","title":"Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number","description":"\u003cp\u003e\u003cb\u003eAn original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThere 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. \u003ci\u003eModeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and\u003c\/i\u003e Z\u003ci\u003e-Number\u003c\/i\u003e presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations.\u003c\/p\u003e \u003cp\u003eThe 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 \u003ci\u003eZ\u003c\/i\u003e-numbers, and the numerical solution of fuzzy differential equations with \u003ci\u003eZ\u003c\/i\u003e-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations\u003c\/li\u003e \u003cli\u003eIncludes an overview of uncertain nonlinear systems for non-specialists\u003c\/li\u003e \u003cli\u003eTeaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development\u003c\/li\u003e \u003cli\u003eReinforces comprehension with illustrations, tables, examples, and simulations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eModeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and\u003c\/i\u003e Z\u003ci\u003e-Number\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eList of Figures xi\u003c\/p\u003e \u003cp\u003eList of Tables xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Fuzzy Equations \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Fuzzy Equations 1\u003c\/p\u003e \u003cp\u003e1.3 Algebraic Fuzzy Equations 3\u003c\/p\u003e \u003cp\u003e1.4 Numerical Methods for Solving Fuzzy Equations 5\u003c\/p\u003e \u003cp\u003e1.4.1 Newton Method 5\u003c\/p\u003e \u003cp\u003e1.4.2 Steepest Descent Method 7\u003c\/p\u003e \u003cp\u003e1.4.3 Adomian Decomposition Method 8\u003c\/p\u003e \u003cp\u003e1.4.4 Ranking Method 9\u003c\/p\u003e \u003cp\u003e1.4.5 Intelligent Methods 10\u003c\/p\u003e \u003cp\u003e1.4.5.1 Genetic Algorithm Method 10\u003c\/p\u003e \u003cp\u003e1.4.5.2 Neural Network Method 11\u003c\/p\u003e \u003cp\u003e1.4.5.3 Fuzzy Linear Regression Model 14\u003c\/p\u003e \u003cp\u003e1.5 Summary 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Fuzzy Differential Equations \u003c\/b\u003e\u003cb\u003e21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Predictor–Corrector Method 21\u003c\/p\u003e \u003cp\u003e2.3 Adomian Decomposition Method 23\u003c\/p\u003e \u003cp\u003e2.4 Euler Method 23\u003c\/p\u003e \u003cp\u003e2.5 Taylor Method 25\u003c\/p\u003e \u003cp\u003e2.6 Runge–Kutta Method 25\u003c\/p\u003e \u003cp\u003e2.7 Finite Difference Method 26\u003c\/p\u003e \u003cp\u003e2.8 Differential Transform Method 28\u003c\/p\u003e \u003cp\u003e2.9 Neural Network Method 29\u003c\/p\u003e \u003cp\u003e2.10 Summary 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Modeling and Control Using Fuzzy Equations \u003c\/b\u003e\u003cb\u003e39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Fuzzy Modeling with Fuzzy Equations 39\u003c\/p\u003e \u003cp\u003e3.1.1 Fuzzy Parameter Estimation with Neural Networks 45\u003c\/p\u003e \u003cp\u003e3.1.2 Upper Bounds of the Modeling Errors 48\u003c\/p\u003e \u003cp\u003e3.2 Control with Fuzzy Equations 52\u003c\/p\u003e \u003cp\u003e3.3 Simulations 59\u003c\/p\u003e \u003cp\u003e3.4 Summary 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Modeling and Control Using Fuzzy Differential Equations \u003c\/b\u003e\u003cb\u003e69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 69\u003c\/p\u003e \u003cp\u003e4.2 Fuzzy Modeling with Fuzzy Differential Equations 69\u003c\/p\u003e \u003cp\u003e4.3 Existence of a Solution 72\u003c\/p\u003e \u003cp\u003e4.4 Solution Approximation using Bernstein Neural Networks 79\u003c\/p\u003e \u003cp\u003e4.5 Solutions Approximation using the Fuzzy Sumudu Transform 83\u003c\/p\u003e \u003cp\u003e4.6 Simulations 85\u003c\/p\u003e \u003cp\u003e4.7 Summary 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 System Modeling with Partial Differential Equations \u003c\/b\u003e\u003cb\u003e101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 101\u003c\/p\u003e \u003cp\u003e5.2 Solutions using Burgers–Fisher Equations 101\u003c\/p\u003e \u003cp\u003e5.3 Solution using Wave Equations 106\u003c\/p\u003e \u003cp\u003e5.4 Simulations 109\u003c\/p\u003e \u003cp\u003e5.5 Summary 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 System Control using Z-numbers \u003c\/b\u003e\u003cb\u003e119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 119\u003c\/p\u003e \u003cp\u003e6.2 Modeling using Dual Fuzzy Equations and Z-numbers 119\u003c\/p\u003e \u003cp\u003e6.3 Controllability using Dual Fuzzy Equations 124\u003c\/p\u003e \u003cp\u003e6.4 Fuzzy Controller 128\u003c\/p\u003e \u003cp\u003e6.5 Nonlinear System Modeling 131\u003c\/p\u003e \u003cp\u003e6.6 Controllability using Fuzzy Differential Equations 131\u003c\/p\u003e \u003cp\u003e6.7 Fuzzy Controller Design using Fuzzy Differential Equations and Z-number 135\u003c\/p\u003e \u003cp\u003e6.8 Approximation using a Fuzzy Sumudu Transform and Z-numbers 138\u003c\/p\u003e \u003cp\u003e6.9 Simulations 139\u003c\/p\u003e \u003cp\u003e6.10 Summary 151\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003eIndex 167\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eWen Yu, PhD,\u003c\/b\u003e is Professor at CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico. He is Associate Editor of \u003ci\u003eIEEE Transactions on Cybernetics, Neurocomputing,\u003c\/i\u003e and\u003ci\u003e Journal of Intelligent and Fuzzy Systems.\u003c\/i\u003e Dr. Yu is a member of the Mexican Academy of Sciences. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRaheleh Jafari\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAn original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eThere 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. \u003ci\u003eModeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and\u003c\/i\u003e Z\u003ci\u003e-Number\u003c\/i\u003e presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations. \u003c\/p\u003e\u003cp\u003eThe 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 \u003ci\u003eZ\u003c\/i\u003e-numbers, and the numerical solution of fuzzy differential equations with \u003ci\u003eZ\u003c\/i\u003e-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book: \u003c\/p\u003e\u003cul\u003e \u003cli\u003ePresents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations\u003c\/li\u003e \u003cli\u003eIncludes an overview of uncertain nonlinear systems for non-specialists\u003c\/li\u003e \u003cli\u003eTeaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development\u003c\/li\u003e \u003cli\u003eReinforces comprehension with illustrations, tables, examples, and simulations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eModeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and\u003c\/i\u003e Z\u003ci\u003e-Number\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989635940581,"sku":"NP9781119491552","price":107.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119491552.jpg?v=1761784901","url":"https:\/\/k12savings.com\/products\/modeling-and-control-of-uncertain-nonlinear-systems-with-fuzzy-equations-and-z-number-isbn-9781119491552","provider":"K12savings","version":"1.0","type":"link"}