An application of pattern recognition based on optimized RBF-DDA neural networks

Authors:
Guoyou Li;Huiguang Li;Min Dong;Changping Sun;Tihua Wu
Affiliations:
College of Electrical Engineering, Yanshan University, Qinhuangdao, China;College of Electrical Engineering, Yanshan University, Qinhuangdao, China;College of Electrical Engineering, Yanshan University, Qinhuangdao, China;College of Electrical Engineering, Yanshan University, Qinhuangdao, China;College of Electrical Engineering, Yanshan University, Qinhuangdao, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

An algorithm of Dynamic Decay Adjustment Radial Basis Function (RBF-DDA) neural networks is presented. It can adaptively get the number of the hidden layer nodes and the center values of data. It resolve the problem of deciding RBF parameters randomly and generalization ability of RBF is improved. When is applied to the system of image pattern recognition, the experimental results show that the recognition rate of the improved RBF neural network still achieves 97.4% even under stronger disturbance. It verifies the good performance of improved algorithm.