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Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes
Joint Authors
Aguilar Cruz, Karen Alicia
Medel Juárez, José de Jesús
Urbieta Parrazales, Romeo
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-13
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
The Artificial Neural Network (ANN) concept is familiar in methods whose task is, for example, the identification or approximation of the outputs of complex systems difficult to model.
In general, the objective is to determine online the adequate parameters to reach a better point-to-point convergence rate, so that this paper presents the parameter estimation for an equivalent ANN (EANN), obtaining a recursive identification for a stochastic system, firstly, with constant parameters and, secondly, with nonstationary output system conditions.
Therefore, in the last estimation, the parameters also have stochastic properties, making the traditional approximation methods not adequate due to their losing of convergence rate.
In order to give a solution to this problematic, we propose a nonconstant exponential forgetting factor (NCEFF) with sliding modes, obtaining in almost all points an exponential convergence rate decreasing.
Theoretical results of both identification stages are performed using MATLAB® and compared, observing improvement when the new proposal for nonstationary output conditions is applied.
American Psychological Association (APA)
Aguilar Cruz, Karen Alicia& Medel Juárez, José de Jesús& Urbieta Parrazales, Romeo. 2016. Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1099685
Modern Language Association (MLA)
Aguilar Cruz, Karen Alicia…[et al.]. Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-6.
https://search.emarefa.net/detail/BIM-1099685
American Medical Association (AMA)
Aguilar Cruz, Karen Alicia& Medel Juárez, José de Jesús& Urbieta Parrazales, Romeo. Equivalent Neural Network Optimal Coefficients Using Forgetting Factor with Sliding Modes. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1099685
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099685