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An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming
Ke Chen; Zhaoxiang Zhang
2016-11-28
会议名称The eighth International Conference on Brain Inspired Cognitive Systems
会议录名称BICS 2016
会议日期28-30 November 2016
会议地点Beijing, China
摘要Encouraged by the success of conventional GradientNet and recently-proposed ZhangNet for online equality-constrained quadratic programming problem, an improved recurrent network and its electronic implementation are firstly proposed and developed in this paper. Exploited in the primal form of quadratic programming with linear equality constraints, the proposed neural model can solve the problem effectively. Moreover, compared to the existing recurrent networks, i.e., GradientNet (GN) and ZhangNet (ZN), our model can theoretically guarantee superior global exponential convergence performance. Robustness performance of our such neural model is also analysed under a large model implementation error, with the upper bound of stead-state solution error estimated. Simulation results demonstrate theoretical analysis on the proposed model for online equality-constrained quadratic programming.
关键词Recurrent Networks Online Equality-constrained Quadratic Programming Global Exponential Convergence Robustness Analysis
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13247
专题类脑智能研究中心
通讯作者Zhaoxiang Zhang
推荐引用方式
GB/T 7714
Ke Chen,Zhaoxiang Zhang. An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming[C],2016.
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