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An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming
Ke Chen; Zhaoxiang Zhang
2016-11-28
Conference NameThe eighth International Conference on Brain Inspired Cognitive Systems
Source PublicationBICS 2016
Conference Date28-30 November 2016
Conference PlaceBeijing, China
AbstractEncouraged 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.
KeywordRecurrent Networks Online Equality-constrained Quadratic Programming Global Exponential Convergence Robustness Analysis
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13247
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Ke Chen,Zhaoxiang Zhang. An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming[C],2016.
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