Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization

  • Authors:

  • Rafik A. Aliev;Witold Pedrycz;Babek G. Guirimov;Rashad R. Aliev;Umit Ilhan;Mustafa Babagil;Sadik Mammadli

  • Affiliations:

  • Azerbaijan State Oil Academy, 20 Azadlyg Ave., Baku, Azerbaijan;University of Alberta, Canada;Azerbaijan State Oil Academy, 20 Azadlyg Ave., Baku, Azerbaijan;Eastern Mediterranean University, Cyprus;Eastern Mediterranean University, Cyprus;Eastern Mediterranean University, Cyprus;Azerbaijan State Oil Academy, Azerbaijan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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Abstract

In many real-world problems involving pattern recognition, system identification and modeling, control, decision making, and forecasting of time-series, available data are quite often of uncertain nature. An interesting alternative is to employ type-2 fuzzy sets, which augment fuzzy models with expressive power to develop models, which efficiently capture the factor of uncertainty. The three-dimensional membership functions of type-2 fuzzy sets offer additional degrees of freedom that make it possible to directly and more effectively account for model's uncertainties. Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient ''If-Then'' rules. The type-2 fuzzy neural networks take advantage of capabilities of fuzzy clustering by generating type-2 fuzzy rule base, resulting in a small number of rules and then optimizing membership functions of type-2 fuzzy sets present in the antecedent and consequent parts of the rules. The clustering itself is realized with the aid of differential evolution. Several examples, including a benchmark problem of identification of nonlinear system, are considered. The reported comparative analysis of experimental results is used to quantify the performance of the developed networks.