title: A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy.
abstract: This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms.
authors: ATR Key Lab of National Defense, shenzhen University, shenzhen 518060, China.
authors: Hui
authors: H
authors: Wen
authors: ATR Key Lab of National Defense, shenzhen University, shenzhen 518060, China.
authors: Weixin
authors: W
authors: Xie
authors: ATR Key Lab of National Defense, shenzhen University, shenzhen 518060, China.
authors: Jihong
authors: J
authors: Pei
mesh_terms: D000465:Algorithms; D000069550:Machine Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013223:Statistics as Topic