Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
473058 | Computers & Mathematics with Applications | 2012 | 9 Pages |
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.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Choon Ki Ahn,