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

Author

Listed:
  • Víctor Daniel Gil Vera
  • Catalina Quintero López
  • Isabel Cristina Puerta Lópera
  • Gabriel Jaime Correa Henao

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.

Suggested Citation

  • Víctor Daniel Gil Vera & Catalina Quintero López & Isabel Cristina Puerta Lópera & Gabriel Jaime Correa Henao, 2019. "Classification of Adolescent Offenders of the Law with Radial Neural Network Bases Function," Modern Applied Science, Canadian Center of Science and Education, vol. 13(10), pages 1-39, October.
    • Handle: RePEc:ibn:masjnl:v:13:y:2019:i:10:p:39
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    References listed on IDEAS

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    1. Guan, Xuemei & Zhu, Yuren & Song, Wenlong, 2016. "Application of RBF neural network improved by peak density function in intelligent color matching of wood dyeing," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 485-490.
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      JEL classification:

      • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
      • Z0 - Other Special Topics - - General

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