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In this paper, we propose a Wavelet Neural Network with Hybrid Learning Approach (WNN-HLA). A novel hybrid learning approach, which combines the on-line partition method (OLPM) and the gradient descent method, is proposed to identify a parsimonious internal structure and adjust the parameters of WNN-HLA model. First, the proposed OLPM is an online method and is a distance-based connectionist clustering method. Unlike the traditional cluster techniques that only consider the total variation to update the one mean and deviation. Second, a back-propagation learning method is used to adjust the parameters for the desired outputs. Several simulation examples have been given to illustrate the performance and effectiveness of the proposed model. The computer simulations demonstrate that the proposed WNN-HLA model performs better than some existing models.

Keywords: wavelet neural networks, on-line partition method, identification, prediction, back-propagation learning algorithm

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Received July 19, 2004; revised January 7 & March 11, 2005; accepted March 23, 2005.
Communicated by Chin-Teng Lin.
^* This research was supported in part by the National Science Council of Taiwan, R.O.C., under grant No. NSC 95-2221-E-324-028-MY2.