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A derived least square fast learning network model

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Abstract

The extreme learning machine (ELM) requires a large number of hidden layer nodes in the training process. Thus, random parameters will exponentially increase and affect network stability. Moreover, the single activation function affects the generalization capability of the network. This paper proposes a derived least square fast learning network (DLSFLN) to solve the aforementioned problems. DLSFLN uses the inheritance of some functions to obtain various activation functions through continuous differentiation of functions. The types of activation functions were increased and the mapping capability of hidden layer neurons was enhanced when the random parameter dimension was maintained. DLSFLN randomly generates the input weights and hidden layer thresholds and uses the least square method to determine the connection weights between the output and the input layers and that between the output and the input nodes. The regression and classification experiments show that DLSFLN has a faster training speed and better training accuracy, generalization capability, and stability compared with other neural network algorithms, such as fast learning network(FLN).

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Acknowledgements

This work was supported by the following grants: Major Program of the National Natural Science Foundation of China (No. 11790282); National Natural Science Foundation of China (No. 11702179,51605315); Young Top-Notch Talents Program of Higher School in Hebei Province (No. BJ2019035); the Independent Project of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures (No. ZZ2020-42); Preferred Hebei Postdoctoral Research Project (No. B2019003017). S&T Promgram of Hebei(No.20310803D); Postgraduate innovation funding project of Shijiazhuang Tiedao University(YC2020030) .

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Meiqi Wang, Sixian Jia, Enli Chen, Shaopu Yang, Pengfei Liu & Zhuang Qi

  2. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Meiqi Wang, Sixian Jia, Enli Chen, Shaopu Yang, Pengfei Liu & Zhuang Qi

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  1. Meiqi Wang
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  2. Sixian Jia
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  3. Enli Chen
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  4. Shaopu Yang
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Correspondence to Enli Chen.

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Wang, M., Jia, S., Chen, E. et al. A derived least square fast learning network model. Appl Intell 50, 4176–4194 (2020). https://doi.org/10.1007/s10489-020-01773-6

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  • DOI: https://doi.org/10.1007/s10489-020-01773-6

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