Neural Net Gains Estimation Based on an Equivalent Model

被引:0
|
作者
Aguilar Cruz, Karen Alicia [1 ]
Medel Juarez, Jose de Jesus [1 ]
Fernandez Munoz, Jose Luis [2 ]
Vigueras Velazquez, Midory Esmeralda [1 ]
机构
[1] Inst Politecn Nacl CIC IPN, Ctr Invest Computac, Ave Juan de Dios Batiz, Mexico City 07738, DF, Mexico
[2] Inst Politecn Nacl CICATA Legaria IPN, Ctr Invest Ciencia Aplicada & Tecnol Avanzada U L, Calzada Legaria 649, Mexico City 11500, DF, Mexico
关键词
D O I
10.1155/2016/1690924
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:10
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