A New Robust Training Law for Dynamic Neural Networks with External Disturbance : An LMI Approach
Author
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-01-16
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance.
Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance.
It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages.
Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.
American Psychological Association (APA)
Ahn, Choon Ki. 2011. A New Robust Training Law for Dynamic Neural Networks with External Disturbance : An LMI Approach. Discrete Dynamics in Nature and Society،Vol. 2010, no. 2010, pp.1-14.
https://search.emarefa.net/detail/BIM-470399
Modern Language Association (MLA)
Ahn, Choon Ki. A New Robust Training Law for Dynamic Neural Networks with External Disturbance : An LMI Approach. Discrete Dynamics in Nature and Society No. 2010 (2010), pp.1-14.
https://search.emarefa.net/detail/BIM-470399
American Medical Association (AMA)
Ahn, Choon Ki. A New Robust Training Law for Dynamic Neural Networks with External Disturbance : An LMI Approach. Discrete Dynamics in Nature and Society. 2011. Vol. 2010, no. 2010, pp.1-14.
https://search.emarefa.net/detail/BIM-470399
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-470399
