Fully complex-valued radial basis function networks: Orthogonal least squares regression and classification

Neurocomputing

Volumn 71, Issue 16-18, 2008, Pages 3421-3433

  Chen, S ^a   Hong, X ^b   Harris, C J ^a   Hanzo, L ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b UNIVERSITY OF READING   (United Kingdom)

Author keywords

Classification; Complex valued radial basis function network; D optimality experimental design; Fisher ratio of class separability measure; Orthogonal least squares algorithm; Regression

Indexed keywords

ALGORITHMS; ATTITUDE CONTROL; COMMUNICATION; COMMUNICATION SYSTEMS; CURVE FITTING; FEEDFORWARD NEURAL NETWORKS; IMAGE SEGMENTATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; MODULATION; NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS; STATISTICS;

CLASSIFICATION; COMPLEX-VALUED RADIAL BASIS FUNCTION NETWORK; D-OPTIMALITY EXPERIMENTAL DESIGN; FISHER RATIO OF CLASS SEPARABILITY MEASURE; ORTHOGONAL LEAST SQUARES ALGORITHM; REGRESSION;

RADIAL BASIS FUNCTION NETWORKS;

AUTOMATION; CLASSIFICATION ALGORITHM; CONFERENCE PAPER; CONTROLLED STUDY; EXPERIMENTAL STUDY; FISHER EXACT TEST; NONLINEAR SYSTEM; PARSIMONY ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; RADIAL BASED FUNCTION; RADIATION BEAM; SIGNAL PROCESSING; WIRELESS COMMUNICATION;

EID: 56549097411     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.12.003     Document Type: Conference Paper

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