Abstract
In this paper, in terms of a linear matrix inequality (LMI), using a delayed Lagrangian network to solve quadratic programming problems, sufficient conditions on delay-dependent and delay-independent are given to guarantee the globally exponential stability of the delayed neural network at the optimal solution. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper improved the ones reported in the existing literatures and the proposed sufficient condition can be checked easily by solving LMI. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.
The work was Supported by Natural Science Foundation of China Three Gorges University(No.604114), Natural Science Foundation of Hubei (Nos.2004ABA055, D200613002) and National Natural Science Foundation of China (No.60574025).
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Jiang, M., Fang, S., Shen, Y., Liao, X. (2007). Improved Results on Solving Quadratic Programming Problems with Delayed Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_38
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DOI: https://doi.org/10.1007/978-3-540-72395-0_38
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