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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, G., Li, H., Dong, M., Sun, C., Wu, T. (2005). An Application of Pattern Recognition Based on Optimized RBF-DDA Neural Networks. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_51
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DOI: https://doi.org/10.1007/11539087_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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