General Recurrent Neural Network for Solving Generalized Linear Matrix Equation
Joint Authors
Cheng, Hong
Guo, Hongliang
Li, Zhan
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-31
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This brief proposes a general framework of the nonlinear recurrent neural network for solving online the generalized linear matrix equation (GLME) with global convergence property.
If the linear activation function is utilized, the neural state matrix of the nonlinear recurrent neural network can globally and exponentially converge to the unique theoretical solution of GLME.
Additionally, as compared with the case of using the linear activation function, two specific types of nonlinear activation functions are proposed for the general nonlinear recurrent neural network model to achieve superior convergence.
Illustrative examples are shown to demonstrate the efficacy of the general nonlinear recurrent neural network model and its superior convergence when activated by the aforementioned nonlinear activation functions.
American Psychological Association (APA)
Li, Zhan& Cheng, Hong& Guo, Hongliang. 2017. General Recurrent Neural Network for Solving Generalized Linear Matrix Equation. Complexity،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1143625
Modern Language Association (MLA)
Li, Zhan…[et al.]. General Recurrent Neural Network for Solving Generalized Linear Matrix Equation. Complexity No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1143625
American Medical Association (AMA)
Li, Zhan& Cheng, Hong& Guo, Hongliang. General Recurrent Neural Network for Solving Generalized Linear Matrix Equation. Complexity. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1143625
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143625