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

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• 作者：Yongqing Yang^a ; ^{yyq640613@gmail.com" class="auth_mail} ; ^{yongqingyang@163.com" class="auth_mail} ; Jinde Cao^b ; ^c ; ^{jdcao@seu.edu.cn" class="auth_mail} ; Xianyun Xu^a ; Manfeng Hu^a ; Yun Gao^a
• 关键词：Neural network ; Convergence ; Stability ; Quadratic programming ; Positive semidefinite
• 刊名：Mathematics and Computers in Simulation
• 出版年：July 2014
• 年：2014
• 卷：101
• 期：Complete
• 页码：103-112
• 全文大小：856 K

文摘

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.