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Structure Perturbation Optimization for Hopfield-Type Neural Networks

  • Gang Yang
  • Xirong Li
  • Jieping Xu
  • Qin Jin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

In this paper, we extract the core idea of state perturbation from Hopfield-type neural networks and define state perturbation formulas to describe the general way of optimization methods. Departing from the core idea and the formulas, we propose a novel optimization method related to neural network structure, named structure perturbation optimization. Our method can produce a structure transforming process to retrain Hopfield-type neural networks to get better problem-solving ability. Experiments validate that our method effectively helps Hopfield-type neural networks to escape from local minima and get superior solutions.

Keywords

stochastic noise structure perturbation Hopfield-type neural network maximum clique problem 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gang Yang
    • 1
  • Xirong Li
    • 1
  • Jieping Xu
    • 1
  • Qin Jin
    • 1
  1. 1.Multimedia Computing Lab, School of InformationRenmin University of ChinaBeijingChina

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