Abstract
Recently, the polynomial echo state network (PESN) has been proposed to incorporate the high order information of input features. However, there are some redundant inputs in PESN, which results in high computational cost. To solve this problem, a backward learning algorithm is designed for PESN, which is denoted as BL-PESN for short. The criterion for input features removing is designed to prune the insignificant input features one by one. The simulation results illustrate that the proposed approach has better prediction accuracy and less testing time than other ESNs.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grants 61603012, 61533002 and 61890930-5, the Beijing Municipal Education Commission Foundation under Grant KM201710005025, the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005), the National Key Research and Development Project under Grants 2018YFC1900800-5.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yang, C., Zhu, X., Qiao, J. (2019). A Backward Learning Algorithm in Polynomial Echo State Networks. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_43
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DOI: https://doi.org/10.1007/978-3-030-32388-2_43
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