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

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 stab...

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Bibliographic Details
Published in:Neural processing letters 2012-04, Vol.35 (2), p.151-158
Main Authors: He, Hanlin, Yan, Lu, Tu, Jianjun
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Cellular communication
Complex Systems
Computational Intelligence
Computer Science
Computer science
control theory
systems
Connectionism. Neural networks
Control systems design
Exact sciences and technology
Neural networks
Optimization
Stabilization
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Summary: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.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-011-9208-7