A Novel Global Hybrid Algorithm for Feedforward Neural Networks

  • Authors:

  • Hongru Li;Hailong Li;Yina Du

  • Affiliations:

  • Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Shenyang, 110004, China;Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Shenyang, 110004, China;Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Shenyang, 110004, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

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Abstract

A novel global optimization hybrid algorithm was presented for training neural networks in this paper. During the course of neural networks training, when the weights are being adjusted with Quasi-Newton(QN) method, the error function may be stuck in a local minimum. In order to solve this problem, a original Filled-Function was created and proved. It was combined with QN method to become a global optimization hybrid algorithm. When the net is trained with our new hybrid algorithm, if error function was tripped in a local minimal point, the new hybrid algorithm was able to help networks out of the local minimal point. After that, the weights could being adjusted until the global minimal point for weights vector was found. One illustrative example is used to demonstrate the effectiveness of the presented scheme.