04989nam 2200721Ia 450 991114292470332120251116231623.09786611956738978128195673612819567329789812811295981281129X(CKB)1000000000538020(EBL)1681318(OCoLC)815756030(SSID)ssj0000138901(PQKBManifestationID)11136680(PQKBTitleCode)TC0000138901(PQKBWorkID)10104852(PQKB)10848557(MiAaPQ)EBC1681318(WSP)00004703(Au-PeEL)EBL1681318(CaPaEBR)ebr10255782(CaONFJC)MIL195673(Perlego)848686(EXLCZ)99100000000053802020020128d2001 uy 0engur|n|---|||||txtccrDifferential neural networks for robust nonlinear control identification, state estimation and trajectory tracking /Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu1st ed.Singapore ;River Edge NJ World Scientificc20011 online resource (455 p.)Includes index.9789810246242 9810246242 Includes bibliographical references and index.Contents ; 0.1 Abstract ; 0.2 Preface ; 0.3 Acknowledgments ; 0.4 Introduction ; 0.4.1 Guide for the Readers ; 0.5 Notations ; I Theoretical Study ; 1 Neural Networks Structures ; 1.1 Introduction ; 1.2 Biological Neural Networks ; 1.3 Neuron Model1.4 Neural Networks Structures 1.4.1 Single-Layer Feedforward Networks ; 1.4.2 Multilayer Feedforward Neural Networks ; 1.4.3 Radial Basis Function Neural Networks ; 1.4.4 Recurrent Neural Networks ; 1.4.5 Differential Neural Networks ; 1.5 Neural Networks in Control1.5.1 Identification 1.5.2 Control ; 1.6 Conclusions ; 1.7 References ; 2 Nonlinear System Identification: Differential Learning ; 2.1 Introduction ; 2.2 Identification Error Stability Analysis for Simplest Differential Neural Networks without Hidden Layers2.2.1 Nonlinear System and Differential Neural Network Model 2.2.2 Exact Neural Network Matching with Known Linear Part ; 2.2.3 Non-exact Neural Networks Modelling: Bounded Unmodelled Dynamics Case2.2.4 Estimation of Maximum Value of Identification Error for Nonlinear Systems with Bounded Unmodelled Dynamics 2.3 Multilayer Differential Neural Networks for Nonlinear System On-line Identification ; 2.3.1 Multilayer Structure of Differential Neural Networks2.3.2 Complete Model Matching CaseThis book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be <i>a priori</i> unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its apNeural networks (Computer science)Nonlinear control theoryNeural networks (Computer science)Nonlinear control theory.006.32Poznyak Alexander S1825463Yu Wenprofesor titular.760806Sanchez Edgar N919962MiAaPQMiAaPQMiAaPQBOOK9911142924703321Differential neural networks for robust nonlinear control4837872UNINA