A Fuzzy Relational Neural Network for Pattern Classification

Abstract

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.

Genetic Fuzzy Relational Neural Network for Infant Cry Classification

“…All experiments are binary classification. Although the problem of infant cry classification is indeed a multiclass problem, and our team has treated it in that way in several previous works( [8,9,10]), but for the present case we present a binary classification because our purpose is to compare our results with a particular similar work wich precissely had that binary approach. Results are compared with the work of Barajas and Reyes [2], that used the same databases.…”

“…In [8,9,10] the FRNN was implemented for infant cry classification. Also, in [2] a proposal is presented to optimize the parameters for the FRNN using a genetic algorithm.…”

Video and audio processing in paediatrics: a review

“…Abnormalities were searched in the cries of newborns with multiple or severe problems during 12 A c c e p t e d M a n u s c r i p t the neonatal period, such as low birth weight, respiratory symptoms, jaundice, apnea, but also infants subsequently victims of presumed sudden infant death syndrome (Golub and Corwin, 1982). In the 2000s, normal and pathological cries began to be automatically labeled thanks to a wide variety of machine learning approaches in the context of deafness (Orozco-García and Reyes-García, 2003;Suaste-Rivas et al, 2004;Rosales-Pérez et al, 2015), hypoxia-based Central Nervous System (CNS) diseases (Ortiz et al, 2004), cleft palate (Lederman et al, 2008) and asphyxia (Suaste-Rivas et al, 2004;Hariharan et al, 2011;Rosales-Pérez et al, 2015).…”

“…Spontaneous cries were also processed: i) in the context of profound hearing loss and/or 13 A c c e p t e d M a n u s c r i p t perinatal asphyxia (Pearce and Taylor, 1993a,b;Schönweiler et al, 1996;Suaste-Rivas et al, 2004;Reyes-Galaviz et al, 2004;Galaviz and García, 2005;Reyes-Galaviz et al, 2005;Barajas-Montiel and Reyes-García, 2006;Verduzco-Mendoza et al, 2012;Wahid et al, 2016), ii) to find possible early signs of autism (Sheinkopf et al, 2012;Orlandi et al, 2012b) iii) in the context of monitoring , iv) to better understand vocal development and early communication (Zeskind et al, 1993;Wermke and Mende, 2009;Borysiak et al, 2016). Cries of hard-of-hearing and healthy infants were compared through duration, amplitude and melody (fundamental frequency fluctuations along a cry) description (Várallyay et al, 2004;Várallyay, 2006Várallyay, , 2007.…”

Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model

“…Among these techniques, artificial neural networks have been one of the most widely used [13,26,41,45]. With the same purpose, the use of support vector machines (SVMs) [7,48], hidden Markov models [33,34], as well as several hybrid approaches that combine fuzzy logic with neural networks [44,[50][51][52], fuzzy logic with support vector machines [6] or evolutionary strategies with neural networks [23] have also been explored. Table 1 summarizes the characteristics of previous studies around the infant cry recognition.…”