Abstract
This paper deals with the guaranteed cost stabilization problem of time-varying delayed cellular neural networks. By introducing the saturation degree function and applying the convex hull theory to handle the activation function, the main contribution of the paper lies in its proposal of a new controller for time-varying delayed CNNs with guaranteed cost according to Lyapunov-Krasovskii theorem, which extends the earlier results and gets less conservative guaranteed cost stabilization. Then we make use of Schur complement to convert the QMI (quadratic matrix inequality) to an LMI (linear matrix inequality) and thus it can be easily used as controller synthesis. The minimization of the guaranteed cost is further studied, and the corresponding LMI criterion to get the controller is given. Finally, numerical examples are given to show the effectiveness of the proposed controller and its corresponding minimization problem.
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Jiang, M., He, H., Yan, L. (2014). A Less Conservative Guaranteed Cost Stabilization of Time-Varying Delayed CNNs. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_1
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DOI: https://doi.org/10.1007/978-3-319-12436-0_1
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