Growing Algorithm of Laguerre Orthogonal Basis Neural Network with Weights Directly Determined

Authors:
Yunong Zhang;Tongke Zhong;Wei Li;Xiuchun Xiao;Chenfu Yi
Affiliations:
School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China 510275;School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China 510275;School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China 510275;School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China 510275 and College of Information, Guangdong Ocean University, Zhanjiang, China 524088;School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China 510275
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

Determination of appropriate neural-network (NN) structure is an important issue for a given learning or training task since the NN performance depends much on it. To remedy the weakness of conventional BP neural networks and learning algorithms, a new Laguerre orthogonal basis neural network is constructed. Based on this special structure, a weights-direct-determination method is derived, which could obtain the optimal weights of such a neural network directly (or to say, just in one step). Furthermore, a growing algorithm is presented for determining immediately the smallest number of hidden-layer neurons. Theoretical analysis and simulation results substantiate the efficacy of such a Laguerre-orthogonal-basis neural network and its growing algorithm based on the weights-direct-determination method.