Annealing robust radial basis function networks with support vector regression for nonlinear inverse system identification with outliers

JVC/Journal of Vibration and Control

Volumn 16, Issue 13, 2010, Pages 1915-1940

  Fu, Yu Yi ^a   Wu, Chia Ju ^b   Jeng, Jin Tsong ^c   Ko, Chia Nan ^a  

^a NAN KAI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^c NATIONAL FORMOSA UNIVERSITY   (Taiwan)

Author keywords

Annealing robust learning algorithm; outliers; radial basis function networks; support vector regression

Indexed keywords

ANNEALING ROBUST LEARNING ALGORITHM; BACKPROPAGATION LEARNING ALGORITHM; ERROR MEASUREMENTS; INITIAL STRUCTURES; INVERSE IDENTIFICATION; INVERSE SYSTEM; LEARNING SPEED; MAGNETO-RHEOLOGICAL DAMPERS; NOISE REJECTION; OUTLIERS; RADIAL BASIS FUNCTIONS; SIMULATION RESULT; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; SYSTEM IDENTIFICATIONS; TWO STAGE; UNIVERSAL APPROXIMATORS;

ANNEALING; ATTITUDE CONTROL; BACKPROPAGATION ALGORITHMS; LEARNING ALGORITHMS; MAGNETOS; NEURAL NETWORKS; OPTIMIZATION; REGRESSION ANALYSIS; RHEOLOGY; STIFFNESS; VECTORS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 78649522021     PISSN: 10775463     EISSN: 17412986     Source Type: Journal    
DOI: 10.1177/1077546309346235     Document Type: Article

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