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An Optimization Method for the Initial Parameters Selection of Fuzzy Cerebellar Model Neural Networks in Parametric Fault Diagnosis

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Abstract

When the initial parameters of the fuzzy cerebellar model neural network (FCMNN) are not properly selected, there is a great possibility that the error function converges to the local minimum region or diverges under the gradient descent back propagation (BP) algorithm, which will affect the classification ability of FCMNN. Aiming at this problem, GA is used to optimize the initial center positions and width of the activation function and weight of FCMNN. After obtaining the optimal initial parameters, the internal parameters of FCMNN can approach the convergence value of minimum error more quickly and accurately through further training of the network, so as to obtain better network learning performance. The introduction of GA to optimize the initial value of FCMNN can effectively reduce the blindness and time cost of manual selection of initial parameters, and further improve the intelligence of neural network diagnosers. The simulation and experimental results show that the classification ability of the GA-FCMNN and GA can effectively find the optimal combination in the set data domain.

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Acknowledgements

The authors appreciate the financial support in part from Fuzhou Science and Technology Program of China under Grant 2019-G-44, and in part from Technology Innovation Fund Support Project by the company of Kehua Hengsheng under Grant KHHS20170416.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

    Qiongbin Lin & Shican Chen

  2. Yuan Ze University, Taoyuan, Taiwan

    Shican Chen & Chih-Min Lin

  3. Quanzhou Power Supply Company, State Grid, Quanzhou, China

    Shican Chen

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  1. Qiongbin Lin
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  2. Shican Chen
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Correspondence to Chih-Min Lin.

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Lin, Q., Chen, S. & Lin, CM. An Optimization Method for the Initial Parameters Selection of Fuzzy Cerebellar Model Neural Networks in Parametric Fault Diagnosis. Int. J. Fuzzy Syst. 22, 2071–2082 (2020). https://doi.org/10.1007/s40815-020-00908-8

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  • DOI: https://doi.org/10.1007/s40815-020-00908-8

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