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Exponential stable learning method for Takagi–Sugeno fuzzy delayed neural networks: A convex optimization approach
- Source :
-
Computers & Mathematics with Applications . Mar2012, Vol. 63 Issue 5, p887-895. 9p. - Publication Year :
- 2012
-
Abstract
- Abstract: In this paper, we propose some new results on stability for Takagi–Sugeno fuzzy delayed neural networks with a stable learning method. Based on the Lyapunov–Krasovskii approach, for the first time, a new learning method is presented to not only guarantee the exponential stability of Takagi–Sugeno fuzzy neural networks with time-delay, but also reduce the effect of external disturbance to a prescribed attenuation level. The proposed learning method can be obtained by solving a convex optimization problem which is represented in terms of a set of linear matrix inequalities (LMIs). An illustrative example is given to demonstrate the effectiveness of the proposed learning method. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 08981221
- Volume :
- 63
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Computers & Mathematics with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 71486560
- Full Text :
- https://doi.org/10.1016/j.camwa.2011.11.054