A recurrent neural network for non-smooth nonlinear programming problems
文献类型：会议论文
| 作者 | Long Cheng ; Zeng-Guang Hou; Min Tan; Xiuqing Wang; Zengshun Zhao; Sanqing Hu
|
| 出版日期 | 2007 |
| 会议日期 | AUG 12-17, 2007 |
| 会议地点 | Orlando |
| 国家 | USA |
| 英文摘要 | A recurrent neural network is proposed for solving non-smooth nonlinear programming problems, which can be regarded as a generalization of the smooth nonlinear programming neural network used in [1]. Based on the non-smooth analysis and the theory of differential inclusions, the proposed neural network is demonstrated to be globally convergent to the exact optimal solution of the original optimization problem. Compared with the existing neural networks, the proposed approach takes both equality and inequality constraints into account, and no penalty parameters have to be estimated beforehand. Therefore, it can solve a larger class of non-smooth programming problems. Finally, several illustrative examples are given to show the effectiveness of the proposed neural network. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/23162]  |
| 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,Min Tan,et al. A recurrent neural network for non-smooth nonlinear programming problems[C]. 见:. Orlando. AUG 12-17, 2007. |
入库方式： OAI收割
来源：自动化研究所
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