Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
| Autor: | Rafik A. Aliev, Mustafa Babagil, Sadik Mammadli, Umit Ilhan, B. G. Guirimov, Witold Pedrycz, R. R. Aliev |
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| Rok vydání: | 2011 |
| Předmět: |
Information Systems and Management
Fuzzy clustering Fuzzy classification Neuro-fuzzy business.industry computer.software_genre Type-2 fuzzy sets and systems Defuzzification Computer Science Applications Theoretical Computer Science Artificial Intelligence Control and Systems Engineering Fuzzy set operations Fuzzy number Data mining Artificial intelligence business computer Software Membership function Mathematics |
| Zdroj: | Information Sciences. 181:1591-1608 |
| ISSN: | 0020-0255 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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