Semantic constraints for membership function optimization

  • Authors:

  • J. V. de Oliveira

  • Affiliations:

  • Dept. of Math. & Comput. Sci., Univ. Da Beira Interior, Covilha

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
  • Year:
  • 1999

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Abstract

The optimization of fuzzy systems using bio-inspired strategies, such as neural network learning rules or evolutionary optimization techniques, is becoming more and more popular. In general, fuzzy systems optimized in such a way cannot provide a linguistic interpretation, preventing us from using one of their most interesting and useful features. This paper addresses this difficulty and points out a set of constraints that when used within an optimization scheme obviate the subjective task of interpreting membership functions. To achieve this a comprehensive set of semantic properties that membership functions should have is postulated and discussed. These properties are translated in terms of nonlinear constraints that are coded within a given optimization scheme, such as backpropagation. Implementation issues and one example illustrating the importance of the proposed constraints are included