A Discrete-time Recurrent Neural Network with Global Exponential Stability for Constrained Linear Variational Inequalities
In this paper, a discrete-time recurrent neural network with global exponential stability is proposed for solving constrained linear variational inequalities. Compared with the existing neural networks for linear variational inequalities, the proposed neural network in this paper has lower model complexity with only one-layer structure. The global exponential stability of the neural network can be guaranteed under some mild conditions. Simulation results show the performance and characteristics of the proposed neural network.
Discrete-time recurrent neural network Globally exponentially stable Linear variational inequalities
LIU Qingshan YANG Wankou
School of Automation, Southeast University, Nanjing 210096, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3296-3301
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）