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Design of sparse Bayesian echo state network for time series prediction

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Abstract

Echo state network (ESN) refers to a novel recurrent neural network with a largely and randomly generated reservoir and a trainable output layer, which has been utilized in the time series prediction. In spite of that, since the output weights are computed by the simple linear regression, there may be an ill-posed problem in the training process for ESN. In order to tackle this issue, a sparse Bayesian ESN (SBESN) is given. The proposed SBESN attempts to estimate the probability of the outputs and trains the network through sparse Bayesian learning, where independent regularization priors should be implied to each weight rather than sharing one prior for all weights. Simulation results illustrate that the SBESN model is insensitivity to reservoir size and completely outperforms other models.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grants 2020YFC1511702, and the National Natural Science Foundation of China under Grants 61771059.

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Authors and Affiliations

  1. Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing, 100192, People’s Republic of China

    Lei Wang & Zhong Su

  2. School of Automation, Beijing Information Science & Technology University, Beijing, 100192, People’s Republic of China

    Lei Wang & Zhong Su

  3. Beijing Jingxinke High-end Information Industry Technology Research Institute Co., Ltd, Beijing, 100192, People’s Republic of China

    Lei Wang

  4. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Lei Wang, Junfei Qiao & Cuili Yang

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

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Wang, L., Su, Z., Qiao, J. et al. Design of sparse Bayesian echo state network for time series prediction. Neural Comput & Applic 33, 7089–7102 (2021). https://doi.org/10.1007/s00521-020-05477-3

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  • DOI: https://doi.org/10.1007/s00521-020-05477-3

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