Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network

Neurocomputing

Volumn 72, Issue 13-15, 2009, Pages 3046-3054

  Zhao, Haiquan ^a   Zhang, Jiashu ^a  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

Functional link artificial neural network; Multilayer perceptron; Nonlinear dynamic system identification; Pipelined architecture

Indexed keywords

COMPUTATIONAL EFFICIENCY; DEEP NEURAL NETWORKS; DYNAMICS; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NEURAL NETWORKS; NONLINEAR DYNAMICAL SYSTEMS; RECURRENT NEURAL NETWORKS; RELIGIOUS BUILDINGS;

COMPUTATIONALLY EFFICIENT; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; FUNCTIONAL EXPANSION; FUNCTIONAL LINK ARTIFICIAL NEURAL NETWORKS; MULTI LAYER PERCEPTRON; PIPELINED ARCHITECTURE; PIPELINED PARALLELISM; REAL TIME RECURRENT LEARNING ALGORITHMS;

DYNAMICAL SYSTEMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; FILTER; FUNCTIONAL LINK ARTIFICIAL NEURAL NETWORK; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; NONLINEAR SYSTEM; PERCEPTRON; PIPELINED FUNCTIONAL LINK ARTIFICIAL RECURRENT NEURAL NETWORK; PRIORITY JOURNAL;

EID: 77955878030     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.04.001     Document Type: Article

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