Abstract
In this paper we describe a genetic fuzzy relational neural network (FRNN) designed for classification tasks. The genetic part of the proposed system determines the best configuration for the fuzzy relational neural network. Besides optimizing the parameters for the FRNN, the fuzzy membership functions are adjusted to fit the problem. The system is tested in several infant cry database reaching results up to 97.55%. The design and implementation process as well as some experiments along with their results are shown.
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Rosales-Pérez, A., Reyes-García, C.A., Gómez-Gil, P. (2011). Genetic Fuzzy Relational Neural Network for Infant Cry Classification. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_31
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DOI: https://doi.org/10.1007/978-3-642-21587-2_31
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