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Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm

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Abstract

Based on the extensive operations of polygonal fuzzy numbers, a GA-BP hybrid algorithm for polygonal fuzzy neural network is designed. Firstly, an optimal solution is obtained by the global searching ability of GA algorithm for the untrained polygonal fuzzy neural network. Secondly, some parameters for connection weights and threshold values are appropriately optimized by using an improved BP algorithm. Finally, through a simulation example, we demonstrate that the GA-BP hybrid algorithm based on the polygonal fuzzy neural network can not only avoid the initial values’ dependence and local convergence of the original BP algorithm, but also overcome a blindness problem of the traditional GA algorithm.

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Authors and Affiliations

  1. School of Mathematics Sciences, Tianjin Normal University, Tianjin, 300387, China

    Yongqiang Yang, Guijun Wang & Yang Yang

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  1. Yongqiang Yang
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  2. Guijun Wang
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  3. Yang Yang
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Corresponding author

Correspondence to Guijun Wang.

Additional information

This work has been supported by National Natural Science Foundation China (Grant No. 61374009)

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Yang, Y., Wang, G. & Yang, Y. Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm. Int. J. Mach. Learn. & Cyber. 5, 815–822 (2014). https://doi.org/10.1007/s13042-013-0224-y

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  • DOI: https://doi.org/10.1007/s13042-013-0224-y

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