Reinforcement learning output feedback NN control using deterministic learning technique

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 3, 2014, Pages 635-641

  Xu, Bin ^a   Yang, Chenguang ^b,c   Shi, Zhongke ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

^c BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Approximate dynamic programming; discrete time system; output feedback control; pure feedback system; radial basis function neural network (RBF NN)

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; DIGITAL CONTROL SYSTEMS; DISCRETE TIME CONTROL SYSTEMS; DYNAMIC PROGRAMMING; FEEDBACK CONTROL; LEARNING ALGORITHMS; RADIAL BASIS FUNCTION NETWORKS; REINFORCEMENT LEARNING;

APPROXIMATE DYNAMIC PROGRAMMING; DISCRETE - TIME SYSTEMS; OUTPUT FEEDBACK CONTROLS; PURE-FEEDBACK SYSTEM; RADIAL BASIS FUNCTION NEURAL NETWORKS;

CONTROLLERS;

ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; COMPUTER SIMULATION; FEEDBACK SYSTEM; HUMAN; NONLINEAR SYSTEM; ONLINE SYSTEM; REINFORCEMENT; TIME;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FEEDBACK; HUMANS; MODELS, NEUROLOGICAL; NONLINEAR DYNAMICS; ONLINE SYSTEMS; REINFORCEMENT (PSYCHOLOGY); TIME FACTORS;

EID: 84897663275     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2292704     Document Type: Article

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