Performance Analysis with Artificial Neural Networks, MLP and RBF Architectures for a Problem of Classification of Children with Autism

Authors

  • Rhyan Ximenes de Brito Instituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE)
  • Carlos Alexandre Rolim Fernandes Universidade Federal do Ceará
  • Márcio André Baima Amora Universidade Federal do Ceará

DOI:

https://doi.org/10.5753/isys.2020.534

Keywords:

Analysis, Neural Networks, Autism

Abstract

Artificial Neural Networks has been outstanding in solving problems in several areas. In this sense, a study was carried out with the implementation and analysis of the Multilayer Perceptron (MLP) and Radial Basis Function Neural Network (RBF) networks, in order to compare results based on training, test and classification of children with or without autism. The methodology was implemented based on 292 samples of individuals from a public database, using the Matlab tool R2015a, divided into 10 parts with cross validation. The results were analyzed considering the different characteristics and behaviors of the implemented networks, obtaining a measure of the quality reached.

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Published

2020-01-06

How to Cite

de Brito, R. X., Fernandes, C. A. R., & Amora, M. A. B. (2020). Performance Analysis with Artificial Neural Networks, MLP and RBF Architectures for a Problem of Classification of Children with Autism. ISys - Brazilian Journal of Information Systems, 13(1), 60–76. https://doi.org/10.5753/isys.2020.534

Issue

Section

Regular articles