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Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
- Source :
-
Information Sciences . May2011, Vol. 181 Issue 9, p1591-1608. 18p. - Publication Year :
- 2011
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 181
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 59185997
- Full Text :
- https://doi.org/10.1016/j.ins.2010.12.014