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A kind of BP neural network algorithm based on grey interval

Author

Listed:
  • Shun-Xiang Wu
  • De-Lin Luo
  • Zhi-Wen Zhou
  • Jian-Huai Cai
  • Yeu-Xiang Shi

Abstract

In order to improve the learning ability of a forward neural network, in this article, we incorporate the feedback back-propagation (FBBP) and grey system theory to consider the learning and training of a neural network new perspective. By reducing the input grey degree we optimise the input of the neural network to make it more rational for learning and training of neural networks. Simulation results verified the efficiency of the proposed algorithm by comparing its performance with that of FBBP and classic back-propagation (BP). The results showed that the proposed algorithm has the characteristics of fast training and strong ability of generalisation and it is an effective learning method.

Suggested Citation

  • Shun-Xiang Wu & De-Lin Luo & Zhi-Wen Zhou & Jian-Huai Cai & Yeu-Xiang Shi, 2011. "A kind of BP neural network algorithm based on grey interval," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(3), pages 389-396.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:3:p:389-396
    DOI: 10.1080/00207720903513582
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    Cited by:

    1. Wu, Lifeng & Liu, Sifeng & Fang, Zhigeng & Xu, Haiyan, 2015. "Properties of the GM(1,1) with fractional order accumulation," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 287-293.

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