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Chebyshev Functional Link Artificial Neural Network Based on Correntropy Induced Metric

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Abstract

In this paper, the Correntropy Induced Metric (CIM) as an alternative to the well-known mean square error (MSE) is employed in Chebyshev functional link artificial neural network (CFLANN) to deal with the noisy training data set and enhance the generalization performance. The MSE performs well under Gaussian noise but it is sensitive to large outliers. The CIM as a local similarity measure, however, can improve significantly the anti-noise ability of CFLANN. The convergence of the proposed algorithm, namely the CFLANN based on CIM (CFLANNCIM), has been analyzed. Simulation results on nonlinear channel identification show that CFLANNCIM can perform much better than the traditional CFLANN and multiple-layer perceptron (MLP) neural networks trained under MSE criterion.

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Acknowledgements

This work was supported by 973 Program (No. 2015CB351703), National Natural Science Foundation of China (61372152), and the Doctoral Scientific Research Foundation of Xi’an University of Technology (No. 103-256081611).

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Authors and Affiliations

  1. Department of Electrical Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Wentao Ma & Jiandong Duan

  2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

    Haiquan Zhao

  3. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Badong Chen

Authors
  1. Wentao Ma
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  2. Jiandong Duan
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  3. Haiquan Zhao
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  4. Badong Chen
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Correspondence to Badong Chen.

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Ma, W., Duan, J., Zhao, H. et al. Chebyshev Functional Link Artificial Neural Network Based on Correntropy Induced Metric. Neural Process Lett 47, 233–252 (2018). https://doi.org/10.1007/s11063-017-9646-y

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