NDLI: A three-phase RBFNN learning algorithm for complex classification

Content Provider	IEEE Xplore Digital Library
Author	Jin Tian Min-Qiang Li Fu-Zan Chen
Copyright Year	2005
Description	Author affiliation: Sch. of Manage., Tianjin Univ., China (Jin Tian; Min-Qiang Li; Fu-Zan Chen)
Abstract	To improve radial basis function neural network (RBFNN) classification ability, this paper proposes a three-phase RBFNN learning algorithm. Firstly, the initial hidden structure of the network is determined by dynamic decay radius clustering algorithm. Then the hidden centers are modified by the sum squared error (SSE) rule, and the radius widths are calculated with the within-cluster and between-cluster distances. Finally the pseudo-inverse algorithm is utilized to train the weights between the hidden layer and the output layer. The experiments are implemented on Iris and Wines datasets, which shows that the proposed RBFNN training algorithm has a better classification ability compared with the conventional methods.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4134
Ending Page	4139
File Size	356937
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527661
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Clustering algorithms Radial basis function networks Iris Artificial neural networks Neural networks Algorithm design and analysis Electronic mail Feedforward systems Feedforward neural networks classification RBFNN decay radius clustering SSE
Content Type	Text
Resource Type	Article

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