Abstract
This paper is concerned with the problem of robust stabilization andH∞control for a class of uncertain neural networks. For the robust stabilization problem, sufficient conditions are derived based on the quadratic convex combination property together with Lyapunov stability theory. The feedback controller we design ensures the robust stability of uncertain neural networks with mixed time delays. We further design a robustH∞controller which guarantees the robust stability of the uncertain neural networks with a givenH∞performance level. The delay-dependent criteria are derived in terms of LMI (linear matrix inequality). Finally, numerical examples are provided to show the effectiveness of the obtained results.
Highlights
Neural networks have received a great deal of attention due to their successful applications in various engineering fields such as associative memory [1], pattern recognition [2], adaptive control, and optimization
This paper is concerned with the problem of robust stabilization and H∞ control for a class of uncertain neural networks
Sakthivel et al [26] studied the problem of robust stabilization and H∞ control for a class of uncertain neural networks with various activation functions and mixed time delays by employing the Lyapunov functional method and the matrix inequality technique
Summary
Neural networks have received a great deal of attention due to their successful applications in various engineering fields such as associative memory [1], pattern recognition [2], adaptive control, and optimization. Sakthivel et al [26] studied the problem of robust stabilization and H∞ control for a class of uncertain neural networks with various activation functions and mixed time delays by employing the Lyapunov functional method and the matrix inequality technique. We consider the problem of robust stabilization and H∞ control for a class of uncertain neural networks by employing a new augmented Lyapunov-Krasovskii functional and estimating its derivative from a novel viewpoint. Rn denotes the n-dimensional Euclidean space; Rn×m is the set of all n × m real matrices; the notation A > 0 (
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