Abstract
In order to improve speed of dynamic response, this paper studied the memory state feedback stabilization for time-varying delayed neural networks systems. By using the second method of Lyapunov, the state feedback controller is given to ensure that the system is asymptotically stable. The related theories are expressed in terms of linear matrix inequalities (LMIs). An example is given to illustrate the effectiveness of the proposed criterion. The simulation results show that this method has excellent control effect. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Zhou, A., Ren, G., Liu, S., & Zhang, Y. (2009). Memory state feedback stabilization for time-varying delayed neural networks systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5551 LNCS, pp. 450–454). https://doi.org/10.1007/978-3-642-01507-6_52
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