Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization |
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Authors: | Rafik A Aliev Witold Pedrycz Sadik Mammadli |
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Affiliation: | a Azerbaijan State Oil Academy, 20 Azadlyg Ave., Baku, Azerbaijan b University of Alberta, Canada c Eastern Mediterranean University, Cyprus d Azerbaijan State Oil Academy, Azerbaijan |
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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. |
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Keywords: | Type-2 fuzzy neural network Fuzzy clustering Type-2 fuzzy rule base Differential evolution optimization |
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