Improved results on H ∞ $\mathcal{H}_{\infty}$ state estimation of static neural networks with interval time-varying delay
Abstract This paper is concerned with the problem of the guaranteed H ∞ $\mathcal{H_{\infty}}$ performance state estimation for static neural networks with interval time-varying delay. Based on a modified Lyapunov-Krasovskii functional and the linear matrix inequality technique, a novel delay-depend...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2016-02-01
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| Series: | Journal of Inequalities and Applications |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13660-016-0990-7 |
| Summary: | Abstract This paper is concerned with the problem of the guaranteed H ∞ $\mathcal{H_{\infty}}$ performance state estimation for static neural networks with interval time-varying delay. Based on a modified Lyapunov-Krasovskii functional and the linear matrix inequality technique, a novel delay-dependent criterion is presented such that the error system is globally asymptotically stable with guaranteed H ∞ $\mathcal{H_{\infty}}$ performance. In order to obtain less conservative results, Wirtinger’s integral inequality and reciprocally convex approach are employed. The estimator gain matrix can be achieved by solving the LMIs. Numerical examples are provided to demonstrate the effectiveness of the proposed method. |
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| ISSN: | 1029-242X |