Improving the generalization performance of RBF neural networks using a linear regression technique

Expert Systems with Applications

Volumn 36, Issue 10, 2009, Pages 12049-12053

  Lin, C L ^a   Wang, J F ^a   Chen, C Y ^b,d   Chen, C W ^c   Yen, C W ^a  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b YUNG TA INSTITUTE OF TECHNOLOGY AND COMMERCE   (Taiwan)

^c SHU TE UNIVERSITY   (Taiwan)

^d NATIONAL PINGTUNG UNIVERSITY   (Taiwan)

Author keywords

Function approximation; Generalization performance; Neural network; Orthogonal least squares; Radial basis function

Indexed keywords

BOOTSTRAP TECHNIQUE; CROSS VALIDATION; EXISTING METHOD; FUNCTION APPROXIMATION; GENERALIZATION PERFORMANCE; GRAM-SCHMIDT ALGORITHMS; HIDDEN LAYER NEURONS; LINEAR REGRESSION TECHNIQUES; ORTHOGONAL LEAST SQUARES; QR ALGORITHMS; RADIAL BASIS FUNCTION; RADIAL BASIS FUNCTION NEURAL NETWORKS; RBF NEURAL NETWORK; REAL PROBLEMS; SOLUTION METHODS; STATISTICAL LINEAR REGRESSIONS;

ALGORITHMS; ATTITUDE CONTROL; NEURAL NETWORKS; NEURONS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 69249205568     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.03.012     Document Type: Article

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