Abstract
In this paper, a new method of fuzzy inference system optimization using a hierarchical genetic algorithm (HGA) is proposed. The fuzzy inference system is used to combine the different responses of modular neural networks (MMNs). In this case, the MMNs are used to perform the human recognition using 4 biometric measures: face, iris, ear, and voice. The main idea is the optimization of some parameters of a fuzzy inference system such as the type of fuzzy logic (FL), type of system, number of membership functions in each input, type of membership functions in each variable, their parameters, and the consequences of the fuzzy rules.
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Sánchez, D., Melin, P., Castillo, O. (2016). Optimization of Type-1 and Type-2 Fuzzy Systems Applied to Pattern Recognition. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_10
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DOI: https://doi.org/10.1007/978-3-319-32229-2_10
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