Volterra system-based neural network modeling by particle swarm optimization approach

Neurocomputing

Volumn 82, Issue , 2012, Pages 179-185

  Yang, Yi Sung ^a   Chang, Wei Der ^b   Liao, Teh Lu ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b SHU TE UNIVERSITY   (Taiwan)

Author keywords

Feedforward neural networks; Nonlinear discrete system modeling; Particle swarm optimization (PSO).; Volterra system

Indexed keywords

ADJUSTABLE PARAMETERS; DESIGN STEPS; DISCRETE DYNAMIC SYSTEM; GLOBAL SOLUTIONS; IDENTIFICATION METHOD; INITIAL CONDITIONS; MEASUREMENT NOISE; MODELING PERFORMANCE; NEURAL NETWORK MODELING; NONLINEAR DISCRETE SYSTEM; OPTIMIZATION PROBLEMS; PARTICLE SWARM; POPULATION SIZES; PSO ALGORITHMS; SYSTEM-BASED; VOLTERRA; VOLTERRA SYSTEMS;

CONTROL SYSTEMS; FEEDFORWARD NEURAL NETWORKS; MATHEMATICAL MODELS; POPULATION STATISTICS;

PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; FEASIBILITY STUDY; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; MEASUREMENT; NOISE; NONLINEAR SYSTEM; PARTICLE SWARM OPTIMIZATION APPROACH; POPULATION SIZE; PRIORITY JOURNAL; VALIDATION PROCESS; VOLTERRA SYSTEM;

EID: 84856445855     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.10.027     Document Type: Article

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