Function approximation using robust wavelet neural networks

Sheng-Tun Li, Shu Ching Chen

研究成果: Conference article同行評審

37 引文 斯高帕斯(Scopus)

摘要

Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators but achieve faster convergence and are capable of dealing with the so-called "curse of dimensionality." In addition, WNN are generalized RBFN. However, the generalization performance of WNN trained by least-squares approach deteriorates when outliers are present. In this paper, we propose a robust wavelet neural network based on the theory of robust regression for dealing with outliers in the framework of function approximation. By adaptively adjusting the number of training data involved during training, the efficiency loss in the presence of Gaussian noise is accommodated. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed network.

原文English
頁(從 - 到)483-488
頁數6
期刊Proceedings of the International Conference on Tools with Artificial Intelligence
出版狀態Published - 2002 12月 1
事件14th International Conference on Tools with Artificial Intelligence - Washington, DC, United States
持續時間: 2002 6月 42002 11月 6

All Science Journal Classification (ASJC) codes

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