Implementation of a Linguistic Fuzzy Relational Neural Network for Detecting Pathologies by Infant Cry Recognition

In this paper we describe the implementation of a fuzzyrelational neural network model. In the model, the input features are represented by their respective fuzzy membership values to linguistic properties. The weights of the connections between input and output nodes are described in terms of their fuzzy relations. The output values during training are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm used is a modified version of the back-propagation algorithm. The system is tested on an infant cry classification problem, in which the objective is to identify pathologies like deafness and asphyxia in recently born babies. The design and implementation of the classifier is presented, as well as results of some experiments.

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