TY - JOUR
T1 - Neural Net Gains Estimation Based on an Equivalent Model
AU - Aguilar Cruz, Karen Alicia
AU - Medel Juárez, José de Jesús
AU - Fernández Muñoz, José Luis
AU - Esmeralda Vigueras Velázquez, Midory
PY - 2016
Y1 - 2016
N2 - 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 A and the proper gain K 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.
AB - 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 A and the proper gain K 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.
UR - http://www.scopus.com/inward/record.url?scp=85044692430&partnerID=8YFLogxK
U2 - 10.1155/2016/1690924
DO - 10.1155/2016/1690924
M3 - Artículo
SN - 1687-5265
VL - 2016
SP - 1690924
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
ER -