Abstract
In this paper we describe the implementation of a fuzzy relational neural network model. In the model, the input features are represented by fuzzy membership, the weights are described in terms of fuzzy relations. The output values are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm is a modified version of back-propagation. The system is tested on an infant cry classification problem, in which the objective is to identify pathologies in recently born babies. © Springer-Verlag 2004.
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CITATION STYLE
Israel, S. R., Reyes-Galaviz, O. F., Alejandro, D. M., & Carlos, R. G. (2004). A fuzzy relational neural network for pattern classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3287, 358–365. https://doi.org/10.1007/978-3-540-30463-0_44
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