Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine |
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Authors: | Jiuwen Cao Zhiping Lin Guang-Bin Huang |
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Affiliation: | (1) Department of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea;(2) Department of Physiology, College of Medicine, Hallym University, Chuncheon, Gangwon, South Korea |
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Abstract: | In this paper, we introduce a new learning method for composite function wavelet neural networks (CFWNN) by combining the
differential evolution (DE) algorithm with extreme learning machine (ELM), in short, as CWN-E-ELM. The recently proposed CFWNN
trained with ELM (CFWNN-ELM) has several promising features. But the CFWNN-ELM may have some redundant nodes due to the number
of hidden nodes assigned a priori and the input weight matrix and the hidden node parameter vector randomly generated once
and never changed during the learning phase. The introduction of DE into CFWNN-ELM is to search for the optimal network parameters
and to reduce the number of hidden nodes used in the network. Simulations on several artificial function approximations, real-world
data regressions and a chaotic signal prediction problem show some advantages of the proposed CWN-E-ELM. Compared with CFWNN-ELM,
CWN-E-ELM has a much more compact network size and Compared with several relevant methods, CWN-E-ELM is able to achieve a
better generalization performance. |
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Keywords: | |
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