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Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine

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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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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Jiuwen Cao, Zhiping Lin & Guang-Bin Huang

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  1. Jiuwen Cao
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  2. Zhiping Lin
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  3. Guang-Bin Huang
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Correspondence to Jiuwen Cao.

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Cao, J., Lin, Z. & Huang, GB. Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine. Neural Process Lett 33, 251–265 (2011). https://doi.org/10.1007/s11063-011-9176-y

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  • DOI: https://doi.org/10.1007/s11063-011-9176-y

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