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Neural Network-based e-Adaptive Dynamic Programming and e-Optimal Control for Nonlinear Systems

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Title: Neural Network-based e-Adaptive Dynamic Programming and e-Optimal Control for Nonlinear Systems
Author(s): Jin, Ning
Advisor(s): Liu, Derong
Contributor(s): Devroye, Natasha; Mazumder, Sudip; Schonfeld, Dan; Darabi, Houshang
Department / Program: Electrical and Computer Engineering
Graduate Major: Electrical and Computer Engineering
Degree Granting Institution: University of Illinois at Chicago
Degree: PhD, Doctor of Philosophy
Genre: Doctoral
Subject(s): e-optimal control e-adaptive dynamic programming performance index function discrete time system wavelet basis function neural network
Abstract: By applying a novel e-optimal control performance index function as an approximation of the optimal performance index function, the e-optimal control theory and e-adaptive dynamic programming algorithms are established. The e-optimal control theory provides a new sense to overcome the "curse of dimensionality" problem and the "over optimal" problem of the optimal control theory. An algorithm of e-adaptive dynamic programming for discrete time systems using neural networks is given for general nonlinear system as well as a fast iterated algorithm is designed for the case that the utility function is quadratic. Furthermore, a novel wavelet basis function neural network (WBFNN) is defined for sequential learning during the numerical simulations of e-adaptive dynamic programming, which is an improvement of the radial basis function neural network (RBFNNs) and the wavelet neural network (WNN).
Issue Date: 2012-12-09
Genre: thesis
URI: http://hdl.handle.net/10027/9046
Rights Information: Copyright 2011 Ning Jin
Date Available in INDIGO: 2012-12-09
Date Deposited: 2011-12
 

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