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A simplified recurrent neural network for solving nonlinear variational inequalities
Long Cheng; Zeng-Guang Hou; Min Tan; Xiuqing Wan
2008
Conference Name IEEE International Joint Conference on Neural Networks (IJCNN)
Conference DateJUN 01-08, 2008
Conference PlaceHong Kong
CountryChina
AbstractA 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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23159
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Long Cheng,Zeng-Guang Hou,Min Tan,et al. A simplified recurrent neural network for solving nonlinear variational inequalities[C],2008.
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