Resumen

SANCHEZ, G.; PEREZ, H.  y  NAKANO, M.. Growing Cell Neural Network using Simultaneous Perturbation. Inf. tecnol. [online]. 2004, vol.15, n.5, pp. 45-52. ISSN 0718-0764.  http://dx.doi.org/10.4067/S0718-07642004000500008.

This paper proposes a multilayer perceptron neural network (MLP) which optimizes both the matrix weights and the numbers of hidden neurons. Initially, the proposed system uses a reduced number of hidden neurons, optimizing the matrix weights by using a simultaneous perturbation algorithm. Once the network converges, its function is analyzed and if this is not as expected, a hidden neuron is added. This process is repeated until achieving the desired functioning. The results obtained show that the proposed system functions similarly to that of a conventional MLP when this has an optimal number of nodes in the hidden layer, decreasing the computational complexity during the training step.

Palabras llave : simultaneous perturbation; multilayer perceptron; growing cell network; pattern recognition.