A new neural network for solving quadratic programming problems with equality and inequality constraints

A new neural network is proposed in this paper for solving quadratic programming problems with equality and inequality constraints. Comparing with the existing neural networks for solving such problems, the proposed neural network has fewer neurons and an one-layer architecture. The proposed neural network is proven to be global convergence. Furthermore, illustrative examples are given to show the effectiveness of the proposed neural network.

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Year of publication:	2014
Authors:	Yang, Yongqing ; Cao, Jinde ; Xu, Xianyun ; Hu, Manfeng ; Gao, Yun
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 101.2014, C, p. 103-112
Publisher:	Elsevier
Subject:	Neural network \| Convergence \| Stability \| Quadratic programming \| Positive semidefinite

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010870381