05540nam 22006494a 450 991087660780332120200520144314.01-280-44804-097866104480430-470-32501-10-471-78181-90-471-78180-0(CKB)1000000000355119(EBL)257211(OCoLC)77521495(SSID)ssj0000097128(PQKBManifestationID)11116623(PQKBTitleCode)TC0000097128(PQKBWorkID)10114472(PQKB)11411223(MiAaPQ)EBC257211(EXLCZ)99100000000035511920050725d2006 uy 0engur|n|---|||||txtccrAdaptive approximation based control unifying neural, fuzzy and traditional adaptive approximation approaches /Jay A. Farrell, Marios M. PolycarpouHoboken, N.J. Wiley-Intersciencec20061 online resource (440 p.)Wiley series in adaptive and learning systems for signal processing, communication and controlDescription based upon print version of record.0-471-72788-1 Includes bibliographical references (p. 401-415) and index.ADAPTIVE 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 Interpolation2.3 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 Types3.1.1 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 Properties3.9 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 Systems4.2.7 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 Convergence4.6 Robust Learning AlgorithmsA 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 bAdaptive and learning systems for signal processing, communications, and control.Adaptive control systemsFeedback control systemsAdaptive control systems.Feedback control systems.629.8/36Farrell Jay633617Polycarpou Marios770684MiAaPQMiAaPQMiAaPQBOOK9910876607803321Adaptive approximation based control1572617UNINA