The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Optimized Multi-Output Fuzzy Neural Networks Based on Interval Type-2 Fuzzy Set for Pattern Recognition

패턴 인식을 위한 Interval Type-2 퍼지 집합 기반의 최적 다중출력 퍼지 뉴럴 네트워크

  • Park, Keon-Jun (Dept. of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
  • Received : 2012.10.17
  • Accepted : 2013.02.26
  • Published : 2013.05.01
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Abstract

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

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References

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