Dynamic topology generation of an artificial neural network of the multilayer perceptron type

  • Héctor Tabares Universidad de Antioquia
  • John Branch Universidad Nacional de Colombia Sede Medellín
  • Jaime Valencia Universidad de Antioquia
Keywords: Artificial neural networks, multi-layer perceptron, topology, architecture

Abstract

This paper deals with an approximate constructive method to find architectures of artificial neuronal network (ANN) of the type Multi-Layer Percetron (MLP) which solves a particular problem. This method is supplemented with the technique of the Forced search of better local minima. The training of the net uses an algorithm basic descending gradient (BDG). Techniques such as repetition of the training and the early stopping (cross validation) are used to improve the results. The evaluation approach is based not only on the learning abilities but also on the generalization of the specific generated architectures of a domain. Experimental results are presented in order to prove the effectiveness of the proposed method. These are compared with architectures found by other methods.

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Author Biographies

Héctor Tabares, Universidad de Antioquia

Departamento de Ingeniería Eléctrica. Facultad de Ingeniería. 

John Branch, Universidad Nacional de Colombia Sede Medellín

Escuela de Sistemas. Facultad de Minas. 

Jaime Valencia, Universidad de Antioquia

Departamento de Ingeniería Eléctrica. Facultad de Ingeniería. 

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Published
2006-08-03
How to Cite
Tabares H., Branch J., & Valencia J. (2006). Dynamic topology generation of an artificial neural network of the multilayer perceptron type. Revista Facultad De Ingeniería Universidad De Antioquia, (38), 146-162. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/343285