Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016, Article ID 1690924, 10 pages
Research Article

Neural Net Gains Estimation Based on an Equivalent Model

1Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Delegación Gustavo A. Madero, 07738 Ciudad de México, DF, Mexico
2Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada U. Legaria, Instituto Politécnico Nacional (CICATA-Legaria-IPN), Calzada Legaria 649, Col. Irrigación, Delegación Miguel Hidalgo, 11500 Ciudad de México, DF, Mexico

Received 30 January 2016; Revised 15 April 2016; Accepted 24 April 2016

Academic Editor: Manuel Graña

Copyright © 2016 Karen Alicia Aguilar Cruz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix and the proper gain into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system.