An Improved Recurrent Network for Online Equality-Constrained Quadratic Programming
文献类型：会议论文
| 作者 | Ke Chen; Zhaoxiang Zhang
|
| 出版日期 | 2016-11-28 |
| 会议日期 | 28-30 November 2016 |
| 会议地点 | Beijing, China |
| 关键词 | Recurrent Networks Online Equality-constrained Quadratic Programming Global Exponential Convergence Robustness Analysis |
| 英文摘要 | 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. |
| 会议录 | BICS 2016
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13247]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 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]. 见:. Beijing, China. 28-30 November 2016. |
入库方式： OAI收割
来源：自动化研究所
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