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Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling

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Abstract

Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely connected reservoir. Since the reservoir size is very large, the collinearity problem may exist in the ESN. To address this problem and get a sparse architecture, an adaptive lasso echo state network (ALESN) is proposed, in which the adaptive lasso algorithm is used to calculate the output weights. The ALESN combines the advantages of quadratic regularization and adaptively weighted lasso shrinkage; furthermore, it has the oracle properties and can deal with the collinearity problem. Meanwhile, to obtain the optimal model, the selection of tuning regularization parameter based on modified Bayesian information criterion is proposed. Simulation results show that the proposed ALESN has better performance and relatively uniform output weights than some other existing methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61533002 and Grant 61603012, in part by the Beijing Municipal Education Commission Foundation under Grant KM201710005025, in part by the Beijing Postdoctoral Research Foundation of China under Grant 2017ZZ-028 and in part by the China Postdoctoral Science Foundation-funded project.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Junfei Qiao, Lei Wang & Cuili Yang

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China

    Junfei Qiao, Lei Wang & Cuili Yang

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  1. Junfei Qiao
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  2. Lei Wang
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  3. Cuili Yang
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Correspondence to Junfei Qiao.

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Qiao, J., Wang, L. & Yang, C. Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling. Neural Comput & Applic 31, 6163–6177 (2019). https://doi.org/10.1007/s00521-018-3420-6

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  • DOI: https://doi.org/10.1007/s00521-018-3420-6

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