000 09056nam a2200601 i 4500
001 8826425
003 IEEE
005 20220712210019.0
006 m o d
007 cr |n|||||||||
008 191003s2019 mau ob 001 eng d
015 _zGBB9C5386 (print)
016 _z019470506 (print)
020 _a9781119491514
_qelectronic
020 _a1119491525
020 _z9781119491552
_qprint
020 _z9781119491521
_qePub ebook
020 _z9781119491545
_qPDF ebook
020 _z1119491541
_qPDF ebook
024 7 _a10.1002/9781119491514
_2doi
035 _a(CaBNVSL)mat08826425
035 _a(IDAMS)0b0000648a1b4542
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
082 0 4 _a629.836
_223
100 1 _aYu, Wen,
_eauthor.
_929546
245 1 0 _aFuzzy modeling and control of uncertain nonlinear systems /
_cWen Yu, Raheleh Jafari.
250 _a1st
264 1 _aHoboken :
_bWiley-IEEE Press,
_c2019.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2019]
300 _a1 PDF (208 pages).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aIEEE Press series on systems science and engineering
500 _a<P>List of Figures xi</p> <p>List of Tables xiii</p> <p>Preface xv</p> <p><b>1 Fuzzy Equations </b><b>1</b></p> <p>1.1 Introduction 1</p> <p>1.2 Fuzzy Equations 1</p> <p>1.3 Algebraic Fuzzy Equations 3</p> <p>1.4 Numerical Methods for Solving Fuzzy Equations 5</p> <p>1.4.1 Newton Method 5</p> <p>1.4.2 Steepest Descent Method 7</p> <p>1.4.3 Adomian Decomposition Method 8</p> <p>1.4.4 Ranking Method 9</p> <p>1.4.5 Intelligent Methods 10</p> <p>1.4.5.1 Genetic Algorithm Method 10</p> <p>1.4.5.2 Neural Network Method 11</p> <p>1.4.5.3 Fuzzy Linear Regression Model 14</p> <p>1.5 Summary 20</p> <p><b>2 Fuzzy Differential Equations </b><b>21</b></p> <p>2.1 Introduction 21</p> <p>2.2 Predictor-Corrector Method 21</p> <p>2.3 Adomian Decomposition Method 23</p> <p>2.4 Euler Method 23</p> <p>2.5 Taylor Method 25</p> <p>2.6 Runge-Kutta Method 25</p> <p>2.7 Finite Difference Method 26</p> <p>2.8 Differential Transform Method 28</p> <p>2.9 Neural Network Method 29</p> <p>2.10 Summary 36</p> <p><b>3 Modeling and Control Using Fuzzy Equations </b><b>39</b></p> <p>3.1 Fuzzy Modeling with Fuzzy Equations 39</p> <p>3.1.1 Fuzzy Parameter Estimation with Neural Networks 45</p> <p>3.1.2 Upper Bounds of the Modeling Errors 48</p> <p>3.2 Control with Fuzzy Equations 52</p> <p>3.3 Simulations 59</p> <p>3.4 Summary 67</p> <p><b>4 Modeling and Control Using Fuzzy Differential Equations </b><b>69</b></p> <p>4.1 Introduction 69</p> <p>4.2 Fuzzy Modeling with Fuzzy Differential Equations 69</p> <p>4.3 Existence of a Solution 72</p> <p>4.4 Solution Approximation using Bernstein Neural Networks 79</p> <p>4.5 Solutions Approximation using the Fuzzy Sumudu Transform 83</p> <p>4.6 Simulations 85</p> <p>4.7 Summary 99</p> <p><b>5 System Modeling with Partial Differential Equations </b><b>101</b></p> <p>5.1 Introduction 101</p> <p>5.2 Solutions using Burgers-Fisher Equations 101</p> <p>5.3 Solution using Wave Equations 106</p> <p>5.4 Simulations 109</p> <p>5.5 Summary 117</p> <p><b>6 System Control using Z-numbers </b><b>119</b></p> <p>6.1 Introduction 119</p> <p>6.2 Modeling using Dual Fuzzy Equations and Z-numbers 119</p> <p>6.3 Controllability using Dual Fuzzy Equations 124</p> <p>6.4 Fuzzy Controller 128</p> <p>6.5 Nonlinear System Modeling 131</p> <p>6.6 Controllability using Fuzzy Differential Equations 131</p> <p>6.7 Fuzzy Controller Design using Fuzzy Differential Equations and Z-number 135</p> <p>6.8 Approximation using a Fuzzy Sumudu Transform and Z-numbers 138</p> <p>6.9 Simulations 139</p> <p>6.10 Summary 151</p> <p>References 153</p> <p>Index 167</p>
505 0 _aList of Figures xi -- List of Tables xiii -- Preface xv -- 1 Fuzzy Equations 1 -- 1.1 Introduction 1 -- 1.2 Fuzzy Equations 1 -- 1.3 Algebraic Fuzzy Equations 3 -- 1.4 Numerical Methods for Solving Fuzzy Equations 5 -- 1.4.1 Newton Method 5 -- 1.4.2 Steepest Descent Method 7 -- 1.4.3 Adomian Decomposition Method 8 -- 1.4.4 Ranking Method 9 -- 1.4.5 Intelligent Methods 10 -- 1.4.5.1 Genetic Algorithm Method 10 -- 1.4.5.2 Neural Network Method 11 -- 1.4.5.3 Fuzzy Linear Regression Model 14 -- 1.5 Summary 20 -- 2 Fuzzy Differential Equations 21 -- 2.1 Introduction 21 -- 2.2 Predictor-Corrector Method 21 -- 2.3 Adomian Decomposition Method 23 -- 2.4 Euler Method 23 -- 2.5 Taylor Method 25 -- 2.6 Runge-Kutta Method 25 -- 2.7 Finite Difference Method 26 -- 2.8 Differential Transform Method 28 -- 2.9 Neural Network Method 29 -- 2.10 Summary 36 -- 3 Modeling and Control Using Fuzzy Equations 39 -- 3.1 Fuzzy Modeling with Fuzzy Equations 39 -- 3.1.1 Fuzzy Parameter Estimation with Neural Networks 45 -- 3.1.2 Upper Bounds of the Modeling Errors 48 -- 3.2 Control with Fuzzy Equations 52 -- 3.3 Simulations 59 -- 3.4 Summary 67 -- 4 Modeling and Control Using Fuzzy Differential Equations 69 -- 4.1 Introduction 69 -- 4.2 Fuzzy Modeling with Fuzzy Differential Equations 69 -- 4.3 Existence of a Solution 72 -- 4.4 Solution Approximation using Bernstein Neural Networks 79 -- 4.5 Solutions Approximation using the Fuzzy Sumudu Transform 83 -- 4.6 Simulations 85 -- 4.7 Summary 99 -- 5 System Modeling with Partial Differential Equations 101 -- 5.1 Introduction 101 -- 5.2 Solutions using Burgers-Fisher Equations 101 -- 5.3 Solution using Wave Equations 106 -- 5.4 Simulations 109 -- 5.5 Summary 117 -- 6 System Control using Z-numbers 119 -- 6.1 Introduction 119 -- 6.2 Modeling using Dual Fuzzy Equations and Z-numbers 119 -- 6.3 Controllability using Dual Fuzzy Equations 124 -- 6.4 Fuzzy Controller 128 -- 6.5 Nonlinear System Modeling 131 -- 6.6 Controllability using Fuzzy Differential Equations 131.
505 8 _a6.7 Fuzzy Controller Design using Fuzzy Differential Equations and Z-number 135 -- 6.8 Approximation using a Fuzzy Sumudu Transform and Z-numbers 138 -- 6.9 Simulations 139 -- 6.10 Summary 151 -- References 153 -- Index 167.
506 _aRestricted to subscribers or individual electronic text purchasers.
520 _aAn 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.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 0 _aCIP data; resource not viewed.
650 0 _aNonlinear systems
_xAutomatic control
_xMathematics.
_929547
650 0 _aFuzzy mathematics.
_93520
655 0 _aElectronic books.
_93294
700 1 _aJafari, Raheleh,
_eauthor.
_929548
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_929549
710 2 _aWiley,
_epublisher.
_929550
776 0 8 _iPrint version:
_z9781119491552
830 0 _aIEEE Press series on systems science and engineering
_98461
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8826425
942 _cEBK
999 _c74617
_d74617