Abstract
In this study, a robust wavelet neural network (WNN) is proposed to approximate functions with outliers. In the proposed methodology, firstly, support vector machine with wavelet kernel function (WSVM) is adopted to determine the initial translation and dilation of a wavelet kernel and the weights of WNNs. Then, an adaptive annealing learning algorithm (AALA) is adopted to accommodate the translations, the dilations, and the weights of the WNNs. In the learning procedure, the AALA is proposed to overcome the problems of initialization and the cut-off points in the robust learning algorithm. Hence, when an initial structure of the WNNs is determined by a support vector regression (SVR) approach, the WNNs with AALA (AALA-WNNs) have fast convergence speed and can robust against outliers. Two examples are simulated to verify the feasibility and efficiency of the proposed algorithm.
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Acknowledgments
This work was supported in part by the National Science Council, Taiwan, R.O.C., under grants NSC 102-2221-E-252-011.
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Kuo, SS., Ko, CN. Adaptive annealing learning algorithm-based robust wavelet neural networks for function approximation with outliers. Artif Life Robotics 19, 186–192 (2014). https://doi.org/10.1007/s10015-014-0150-4
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DOI: https://doi.org/10.1007/s10015-014-0150-4