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- W2385754300 abstract "The key to construct a radial basis function(RBF)network is to select reasonable hidden center vectors,RBF width and hidden node number.In order to design a RBF network with parsimonious structure and good generalization,a new two-level learning hierarchy for designing RBF networks is proposed.At the lower level in this method,a parsimonious RBF model is constructed by an integrated algorithm(ROLS+D-opt)which combines regularized orthogonal least squares (ROLS)with D-optimality experimental design(D-opt).At the upper level,particle swarm optimization(PSO)is used to search the optimal combination of three important learning parameters,i.e.,the RBF width,the regularized parameter and D-optimality weight parameter,which influence the network's generalization ability.Simulation results show the effectiveness of the proposed method." @default.
- W2385754300 created "2016-06-24" @default.
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- W2385754300 date "2008-01-01" @default.
- W2385754300 modified "2023-09-25" @default.
- W2385754300 title "A two-level learning hierarchy for the radial basis function networks" @default.
- W2385754300 hasPublicationYear "2008" @default.
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