A simplified recurrent neural network for solving nonlinear variational inequalities

	A simplified recurrent neural network for solving nonlinear variational inequalities
	Long Cheng; Zeng-Guang Hou; Min Tan; Xiuqing Wan
	2008
会议名称	IEEE International Joint Conference on Neural Networks (IJCNN)
会议日期	JUN 01-08, 2008
会议地点	Hong Kong
会议举办国	China
摘要	A recurrent neural network is proposed to deal with the nonlinear variational inequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is reduced dramatically. In addition, the proposed neural network can also be applied to the constrained optimization problems, and it is proved that the convex condition on the objective function of the optimization problem can be relaxed. Finally, the satisfactory performance of the proposed approach is demonstrated by simulation examples.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/23159
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 simplified recurrent neural network for solving nonlinear variational inequalities[C],2008.