Abstract
In this paper a new method for Modular Granular Neural Network (MGNN) optimization with a granular approach is presented. A Particle Swarm Optimization technique is proposed to perform the granulation of information with a fuzzy dynamic parameters adaptation to prevent stagnation. The proposed fuzzy inference system seeks to adjust some PSO parameters such as w, C1 and C2 to ensure that the parameters have adequate values depending on the current behavior of the particles. The objective of the proposed PSO is design optimal MGNN architectures. The modular granular neural networks are applied to human recognition based on iris biometrics, where a benchmark database is used and the objective function in this work is the minimization of the error of recognition.
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Sánchez, D., Melin, P., Castillo, O. (2018). Particle Swarm Optimization with Fuzzy Dynamic Parameters Adaptation for Modular Granular Neural Networks. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-319-66827-7_25
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DOI: https://doi.org/10.1007/978-3-319-66827-7_25
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