吉林大学学报(工学版) ›› 2001, Vol. ›› Issue (1): 66-70.

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A Hybrid Evolutionary Neural Network Method in Identifying Nonlinear Characteristics of Packaging

YANG Xiao-wei1, LIANG Yan-chun2, GONG Wen-ying3, ZHOU Chun-guang4   

  1. 1. Dept of Applied Mathematics, South China University of Technology, Guangzhou 510640, China;
    2. Open Lab of CAD/CAM Technology for Modern Manu facturing, Academia Sinica, Shenyang 110003, China;
    3. Dept of Mathematics, Jilin University, Qianwei Campus, Changchun 130012, China;
    4. Dept of Computer Science, Jilin University, Qianwei Campus, Changchun 130012, China
  • Received:2000-06-15 Online:2001-01-25

Abstract: A hybrid method using the genetic algorithm(GA) and the error back propagation (BP) algorithm to train structural neural networks is presented and applied to the identification of nonlinear characteristics of packaging cushioning in this paper.A novel adaptive mutation technique and a scheme to shift the training of the network from the GA to the BP algorithm are proposed.The simulated results on the two typical models of packaging cushioning materials show that the nonlinear charateristics can be identified perfectly.This method also provides a feasible way of hybrid training of neural networks.

Key words: genetic evolution, packaging cushioning, nonlinear characteristics, BP algorithm, structural neural networks, model identification

CLC Number: 

  • O327
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