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[null,"1.","Record Nr.",null,"UNINA9910830081303321"]
[null,null,"Autore",null,"Farrell Jay"]
[null,null,"Titolo",null,"Adaptive approximation based control [[electronic resource] ] : unifying neural, fuzzy and traditional adaptive approximation approaches / / Jay A. Farrell, Marios M. Polycarpou"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : Wiley-Interscience, c2006"]
[null,null,"ISBN",null,"1-280-44804-0"]
[{"$nulls":4},"9786610448043"]
[{"$nulls":4},"0-470-32501-1"]
[{"$nulls":4},"0-471-78181-9"]
[{"$nulls":4},"0-471-78180-0"]
[null,null,"Descrizione fisica",null,"1 online resource (440 p.)"]
[null,null,"Collana",null,"Wiley series in adaptive and learning systems for signal processing, communication and control"]
[null,null,"Disciplina",null,"629.8"]
[null,null,"Soggetti",null,"Adaptive control systems"]
[{"$nulls":4},"Feedback control systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 401-415) and index."]
[null,null,"Nota di contenuto",null,"ADAPTIVE APPROXIMATlON BASED CONTROL; CONTENTS; Preface; 1 Introduction; 1.1 Systems and Control Terminology; 1.2 Nonlinear Systems; 1.3 Feedback Control Approaches; 1.3.1 Linear Design; 1.3.2 Adaptive Linear Design; 1.3.3 Nonlinear Design; 1.3.4 Adaptive Approximation Based Design; 1.3.5 Example Summary; 1.4 Components of Approximation Based Control; 1.4.1 Control Architecture; 1.4.2 Function Approximator; 1.4.3 Stable Training Algorithm; 1.5 Discussion and Philosophical Comments; 1.6 Exercises and Design Problems; 2 Approximation Theory; 2.1 Motivating Example; 2.2 Interpolation"]
[{"$nulls":4},"2.3 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 "]
[{"$nulls":4},"Structures; 3.1 Model Types"]
[{"$nulls":4},"3.1.1 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"]
[{"$nulls":4},"3.9 Further Reading3.10 Exercises and Design Problems; 4 Parameter Estimation Methods; 4.1 Formulation for Adaptive Approximation; 4.1.1 Illustrative Example; 4.1.2 Motivating Simulation Examples; 4.1.3 Problem Statement; 4.1.4 Discussion of Issues in Parametric Estimation; 4.2 Derivation of Parametric Models; 4.2.1 Problem Formulation for Full-State Measurement; 4.2.2 Filtering Techniques; 4.2.3 SPR Filtering; 4.2.4 Linearly Parameterized Approximators; 4.2.5 Parametric Models in State Space Form; 4.2.6 Parametric Models of Discrete-Time Systems"]
[{"$nulls":4},"4.2.7 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"]
[{"$nulls":4},"4.6 Robust Learning Algorithms"]
[null,null,null,"Sommario/riassunto","A 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"]