Abstract
In this paper, a neural network model for solving convex nonlinear programming problems is investigated based on the Fischer-Burmeister function and steepest descent method. The proposed neural network is proved to be stable in the sense of Lyapunov and can converge to an optimal solution of the original optimization problem. An example shows the effectiveness of the proposed neural network model. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Yang, Y., Xu, X., & Zhu, D. (2006). The neural network for solving convex nonlinear programming problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4113 LNCS-I, pp. 494–499). Springer Verlag. https://doi.org/10.1007/11816157_62
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