A recurrent neural network for non-smooth nonlinear programming problems

	A recurrent neural network for non-smooth nonlinear programming problems
	Long Cheng; Zeng-Guang Hou; Min Tan; Xiuqing Wang; Zengshun Zhao; Sanqing Hu
	2007
会议名称	IEEE International Joint Conference on Neural Networks (IJCNN)
会议日期	AUG 12-17, 2007
会议地点	Orlando
会议举办国	USA
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.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/23162
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,Min Tan,et al. A recurrent neural network for non-smooth nonlinear programming problems[C],2007.