Impacts of perturbations of training patterns on two fuzzy associative memories based on t-norms

  • Authors:

  • Wei-Hong Xu;Guo-Ping Chen;Zhong-Ke Xie

  • Affiliations:

  • College of Mathematics and Computer Science, Jishou University, Jishou, China;College of Mathematics and Computer Science, Jishou University, Jishou, China;College of Computer and Communications Engineering, Changsha University of Science and Technology, Changsha, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

In general, there is perturbation between collected training pattern and its corresponding actual pattern in real world, such perturbation may cause disadvantage to performance of a fuzzy neural network, therefore a type of robustness of fuzzy associative memories (FAMs) is proposed correlative with the perturbations of training patterns in the paper, then it is pointed out that using the maximum-weight-matrix learning algorithm, a Max-T0FAM has poor such robustness, however, a Max-TL FAM holds good robustness, where the two FAMs are based on t-norm T0and Lukasiewicz t-norm, respectively. Finally, a simulation experiment validates our theoretical results.