Knowledge Commons of Institute of Automation,CAS
Solving convex optimization problems using recurrent neural networks in finite time | |
Long Cheng![]() ![]() ![]() | |
2009 | |
会议名称 | International Joint Conference on Neural Networks |
会议日期 | JUN 14-19, 2009 |
会议地点 | Atlanta |
会议举办国 | USA |
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. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23154 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
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],2009. |
条目包含的文件 | 条目无相关文件。 |
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