Deterministic Learning Theory for Identification, Recognition, and Control
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Deterministic Learning Theory for Identification, Recognition, and Control
For Identiflcation, Recognition, and Conirol
Wang, Cong; Lewis, Frank L.; Hill, David J.
Taylor & Francis Ltd
10/2017
207
Mole
Inglês
9781138112056
15 a 20 dias
400
Descrição não disponível.
Introduction. RBF Networks and the PE Condition. Locally Accurate Identification of Nonlinear Systems. Learning from Closed-Loop Neural Control. Rapid Recognition of Dynamical Patterns. Deterministic Learning using Output Measurements. Applications of Deterministic Learning. Conclusions.
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Adaptive NN Control;NN Approximation Error;RBF Network;Deterministic Learning;Direct Adaptive NN Control;Exponential Stability;dynamical patterns;Pe Condition;Human-Like Learning;Knowledge Acquisition;pattern-based learning;Estimated State Trajectory;David J. Hill;radial basis function networks;Van Der Pol Oscillator;closed-loop neural control;NN Approximation;deterministic learning theory;Nonlinear Observer Design;dynamical parallel distributed processing model;Brunovsky Form;Duffing Oscillator;LTV System;Unknown Smooth Nonlinear Function;Dynamical Pattern;Neural Weights;RBF Approximation;UGES;High Gain Observer;Observation Error;RBF Network Model;Strict Feedback Systems;State Observation Error;NN Input
Introduction. RBF Networks and the PE Condition. Locally Accurate Identification of Nonlinear Systems. Learning from Closed-Loop Neural Control. Rapid Recognition of Dynamical Patterns. Deterministic Learning using Output Measurements. Applications of Deterministic Learning. Conclusions.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Adaptive NN Control;NN Approximation Error;RBF Network;Deterministic Learning;Direct Adaptive NN Control;Exponential Stability;dynamical patterns;Pe Condition;Human-Like Learning;Knowledge Acquisition;pattern-based learning;Estimated State Trajectory;David J. Hill;radial basis function networks;Van Der Pol Oscillator;closed-loop neural control;NN Approximation;deterministic learning theory;Nonlinear Observer Design;dynamical parallel distributed processing model;Brunovsky Form;Duffing Oscillator;LTV System;Unknown Smooth Nonlinear Function;Dynamical Pattern;Neural Weights;RBF Approximation;UGES;High Gain Observer;Observation Error;RBF Network Model;Strict Feedback Systems;State Observation Error;NN Input
