Abstract
In this paper a new approach for construction of neuro-fuzzy systems for nonlinear classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems which allow us to extend notation of rules with weights of fuzzy sets. The proposed approach uses possibilities of hybrid evolutionary algorithm and interpretability criteria of expert knowledge. These criteria include not only complexity of the system, but also semantics of the rules. The approach presented in our paper was tested on typical nonlinear classification simulation problems.
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Keywords
- Particle Swarm Optimization
- Fuzzy System
- Fuzzy Rule
- Imperialist Competitive Algorithm
- Hybrid Evolutionary Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Łapa, K., Cpałka, K., Galushkin, A.I. (2015). A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_41
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DOI: https://doi.org/10.1007/978-3-319-19324-3_41
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