A novel generalized congruence neural networks

Authors:
Yong Chen;Guoyin Wang;Fan Jin;Tianyun Yan
Affiliations:
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;School of Computer and Communication Engineering, Southwest Jiaotong University, Chengdu, China;School of Computer and Communication Engineering, Southwest Jiaotong University, Chengdu, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

All existing architectures and learning algorithms for Generalized Congruence Neural Network (GCNN) seem to have some shortages or lack rigorous theoretical foundation. In this paper, a novel GCNN architecture (BPGCNN) is proposed. A new error back-propagation learning algorithm is also developed for the BPGCNN. Experimental results on some benchmark problems show that the proposed BPGCNN performs better than standard sigmoidal BPNN and some improved versions of BPNN in convergence speed and learning capability, and can overcome the drawbacks of other existing GCNNs.