Dynamic nonlinear system identification using a wiener-type recurrent network with OKID algorithm

Journal of Information Science and Engineering

Volumn 24, Issue 3, 2008, Pages 891-905

  Wang, Jeen Shing ^a   Hsu, Yu Liang ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Dynamic system identification; Minimal state space model realization; Observer Kalman filter identification; Recurrent neural networks; Wiener models

Indexed keywords

ALGORITHMS; BOOLEAN FUNCTIONS; COMPUTATIONAL METHODS; COMPUTER NETWORKS; COMPUTER SIMULATION; DIFFERENCE EQUATIONS; DYNAMIC MODELS; DYNAMIC PROGRAMMING; DYNAMICAL SYSTEMS; DYNAMICS; EIGENVALUES AND EIGENFUNCTIONS; EQUATIONS OF STATE; EVOLUTIONARY ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); LEARNING SYSTEMS; LINEAR EQUATIONS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; MECHANICS; METROPOLITAN AREA NETWORKS; MICROFLUIDICS; NETWORK PROTOCOLS; NEURAL NETWORKS; NONLINEAR EQUATIONS; NONLINEAR PROGRAMMING; NONLINEAR SYSTEMS; RECURRENT NEURAL NETWORKS; SCHEDULING ALGORITHMS; STATE SPACE METHODS;

(P ,P ,T) MEASUREMENTS; INPUT/OUTPUT (I/O); NETWORK STRUCTURES; NON LINEAR TRANSFORMATIONS; NON-LINEAR DYNAMIC SYSTEMS; NON-LINEAR SYSTEM IDENTIFICATION; RECURRENT NETWORKS; STATE-SPACE EQUATIONS; WIENER MODELS;

LEARNING ALGORITHMS;

EID: 44649174348     PISSN: 10162364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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