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. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Jiang, M., Fang, S., Shen, Y., & Liao, X. (2007). Improved results on solving quadratic programming problems with delayed neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4493 LNCS, pp. 292–301). Springer Verlag. https://doi.org/10.1007/978-3-540-72395-0_38
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
