An effective learning of neural network by using RFBP learning algorithm

Authors:
Yu-Ju Chen;Tsung-Chuan Huang;Rey-Chue Hwang
Affiliations:
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan, ROC and Department of Industrial Engineering and Management, Cheng-Shiu University, Kaohsiung 833, Tai ...;Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan, ROC;Department of Electrical Engineering, I-Shou University, Kaohsiung 84008, Taiwan, ROC
Venue:
Information Sciences—Informatics and Computer Science: An International Journal
Year:
2004

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Abstract

In this paper, an effective learning of neural network by using random fuzzy back-propagation (RFBP) learning algorithm is developed. Based on this new learning algorithm, neural network not only has an accurate learning capability, but also can increase the probability of escaping from the local minimum while neural network is training. For demonstrating the new algorithm we developed has its outperformance, the classifications of the non-convex in two dimensions (NC2) problem are simulated. For comparison, the same simulations by using conventional back-propagation (BP) learning algorithm with constant pairs of learning rate (α = 0.1-0.9) and momentum (ξ = 0.1-0.9) and stochastic BP learning are also performed.