LEADER 05560nam 22006494a 450 001 9910841512703321 005 20230828201130.0 010 $a1-280-44804-0 010 $a9786610448043 010 $a0-470-32501-1 010 $a0-471-78181-9 010 $a0-471-78180-0 035 $a(CKB)1000000000355119 035 $a(EBL)257211 035 $a(OCoLC)77521495 035 $a(SSID)ssj0000097128 035 $a(PQKBManifestationID)11116623 035 $a(PQKBTitleCode)TC0000097128 035 $a(PQKBWorkID)10114472 035 $a(PQKB)11411223 035 $a(MiAaPQ)EBC257211 035 $a(EXLCZ)991000000000355119 100 $a20050725d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdaptive approximation based control$b[electronic resource] $eunifying neural, fuzzy and traditional adaptive approximation approaches /$fJay A. Farrell, Marios M. Polycarpou 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (440 p.) 225 1 $aWiley series in adaptive and learning systems for signal processing, communication and control 300 $aDescription based upon print version of record. 311 $a0-471-72788-1 320 $aIncludes bibliographical references (p. 401-415) and index. 327 $aADAPTIVE APPROXIMATlON BASED CONTROL; CONTENTS; Preface; 1 Introduction; 1.1 Systems and Control Terminology; 1.2 Nonlinear Systems; 1.3 Feedback Control Approaches; 1.3.1 Linear Design; 1.3.2 Adaptive Linear Design; 1.3.3 Nonlinear Design; 1.3.4 Adaptive Approximation Based Design; 1.3.5 Example Summary; 1.4 Components of Approximation Based Control; 1.4.1 Control Architecture; 1.4.2 Function Approximator; 1.4.3 Stable Training Algorithm; 1.5 Discussion and Philosophical Comments; 1.6 Exercises and Design Problems; 2 Approximation Theory; 2.1 Motivating Example; 2.2 Interpolation 327 $a2.3 Function Approximation2.3.1 Offline (Batch) Function Approximation; 2.3.2 Adaptive Function Approximation; 2.4 Approximator Properties; 2.4.1 Parameter (Non) Linearity; 2.4.2 Classical Approximation Results; 2.4.3 Network Approximators; 2.4.4 Nodal Processors; 2.4.5 Universal Approximator; 2.4.6 Best Approximator Property; 2.4.7 Generalization; 2.4.8 Extent of Influence Function Support; 2.4.9 Approximator Transparency; 2.4.10 Haar Conditions; 2.4.11 Multivariable Approximation by Tensor Products; 2.5 Summary; 2.6 Exercises and Design Problems; 3 Approximation Structures; 3.1 Model Types 327 $a3.1.1 Physically Based Models3.1.2 Structure (Model) Free Approximation; 3.1.3 Function Approximation Structures; 3.2 Polynomials; 3.2.1 Description; 3.2.2 Properties; 3.3 Splines; 3.3.1 Description; 3.3.2 Properties; 3.4 Radial Basis Functions; 3.4.1 Description; 3.4.2 Properties; 3.5 Cerebellar Model Articulation Controller; 3.5.1 Description; 3.5.2 Properties; 3.6 Multilayer Perceptron; 3.6.1 Description; 3.6.2 Properties; 3.7 Fuzzy Approximation; 3.7.1 Description; 3.7.2 Takagi-Sugeno Fuzzy Systems; 3.7.3 Properties; 3.8 Wavelets; 3.8.1 Multiresolution Analysis (MRA); 3.8.2 MRA Properties 327 $a3.9 Further Reading3.10 Exercises and Design Problems; 4 Parameter Estimation Methods; 4.1 Formulation for Adaptive Approximation; 4.1.1 Illustrative Example; 4.1.2 Motivating Simulation Examples; 4.1.3 Problem Statement; 4.1.4 Discussion of Issues in Parametric Estimation; 4.2 Derivation of Parametric Models; 4.2.1 Problem Formulation for Full-State Measurement; 4.2.2 Filtering Techniques; 4.2.3 SPR Filtering; 4.2.4 Linearly Parameterized Approximators; 4.2.5 Parametric Models in State Space Form; 4.2.6 Parametric Models of Discrete-Time Systems 327 $a4.2.7 Parametric Models of Input-Output Systems4.3 Design of Online Learning Schemes; 4.3.1 Error Filtering Online Learning (EFOL) Scheme; 4.3.2 Regressor Filtering Online Learning (RFOL) Scheme; 4.4 Continuous-Time Parameter Estimation; 4.4.1 Lyapunov-Based Algorithms; 4.4.2 Optimization Methods; 4.4.3 Summary; 4.5 Online Learning: Analysis; 4.5.1 Analysis of LIP EFOL Scheme with Lyapunov Synthesis Method; 4.5.2 Analysis of LIP RFOL Scheme with the Gradient Algorithm; 4.5.3 Analysis of LIP RFOL Scheme with RLS Algorithm; 4.5.4 Persistency of Excitation and Parameter Convergence 327 $a4.6 Robust Learning Algorithms 330 $aA highly accessible and unified approach to the design and analysis of intelligent control systemsAdaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox.Mixing approximation theory, parameter estimation, and feedback control, this book presents a unified approach designed to enable readers to apply adaptive approximation based control to existing systems, and, more importantly, to gain enough intuition and understanding to manipulate and combine it with other control tools for applications that have not been encountered b 410 0$aAdaptive and learning systems for signal processing, communications, and control. 606 $aAdaptive control systems 606 $aFeedback control systems 615 0$aAdaptive control systems. 615 0$aFeedback control systems. 676 $a629.8 700 $aFarrell$b Jay$0633617 701 $aPolycarpou$b Marios$0770684 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910841512703321 996 $aAdaptive approximation based control$91572617 997 $aUNINA