Solving convex optimization problems using recurrent neural networks in finite time
文献类型：会议论文
| 作者 | Long Cheng ; Zeng-Guang Hou; Noriyasu Homma; Min Tan; Madan M. Gupta
|
| 出版日期 | 2009 |
| 会议日期 | JUN 14-19, 2009 |
| 会议地点 | Atlanta |
| 国家 | USA |
| 英文摘要 | A recurrent neural network is proposed to deal with the convex optimization problem. By employing a specific nonlinear unit, the proposed neural network is proved to be convergent to the optimal solution in finite time, which increases the computation efficiency dramatically. Compared with most of existing stability conditions, i.e., asymptotical stability and exponential stability, the obtained finite-time stability result is more attractive, and therefore could be considered as a useful supplement to the current literature. In addition, a switching structure is suggested to further speed up the neural network convergence. Moreover, by using the penalty function method, the proposed neural network can be extended straightforwardly to solving the constrained optimization problem. Finally, the satisfactory performance of the proposed approach is illustrated by two simulation examples. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/23154]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 推荐引用方式 GB/T 7714 | Long Cheng,Zeng-Guang Hou,Noriyasu Homma,et al. Solving convex optimization problems using recurrent neural networks in finite time[C]. 见:. Atlanta. JUN 14-19, 2009. |
入库方式： OAI收割
来源：自动化研究所
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