Classification of Adolescent Offenders of the Law with Radial Neural Network Bases Function

Víctor Daniel Gil Vera, Catalina Quintero López, Isabel Cristina Puerta Lópera and Gabriel Jaime Correa Henao

Modern Applied Science, 2019, vol. 13, issue 10, 39

Abstract: Radial Basis Function Neural Networks (RBFNN) are a type of artificial neuronal network (ANN) that estimate the output of the function taking as a reference the distance to a point called center. This paper presents a RBFNN for the classification of adolescents’ offenders of the law according to their dangerousness level, that have been admitted to the Specialized Attention Center (SAC), “El Redentor” in Bogotá, Colombia in the year 2017. This classification may be utilized by psychosocial teams of the SAC in order to customize and make more effective the therapeutic pedagogical treatment. The ANN developed is particularly good for identifying adolescents with a high, low and null dangerousness level.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:13:y:2019:i:10:p:39

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