Guaranteed Cost Stabilization of Time-varying Delay Cellular Neural Networks via Riccati Inequality Approach

Authors:
Hanlin He;Lu Yan;Jianjun Tu
Affiliations:
College of Science, Naval University of Engineering, Wuhan, China 430033;College of Science, Naval University of Engineering, Wuhan, China 430033;College of Science, Naval University of Engineering, Wuhan, China 430033
Venue:
Neural Processing Letters
Year:
2012

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Abstract

This letter deals with the guaranteed cost stabilization of time---varying delay cellular neural networks (DCNNs). Based on the Razumikhin theorem and via applying the zoned discussion and maximax synthesis method in DCNNs, the quadratic Riccati matrix inequality criterion for the guaranteed cost stabilization controller is designed to stabilize the given chaotic DCNNs. The minimization of the guaranteed cost of stabilization for the DCNNs is also given. Finally, numerical examples are given to show the effectiveness of proposed guaranteed cost stabilization control and its corresponding minimization problem.