Liu Qingshan, Cao Jinde
School of Automation, Southeast University, Nanjing 210096, China.
IEEE Trans Syst Man Cybern B Cybern. 2010 Jun;40(3):928-38. doi: 10.1109/TSMCB.2009.2033565. Epub 2009 Nov 20.
Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.
基于投影算子，提出了一种用于求解扩展广义变分不等式（EGVIs）的递归神经网络。基于李雅普诺夫方法提供了充分条件，以确保所提出神经网络的全局收敛性。与现有的用于变分不等式的神经网络相比，所提出的神经网络是文献中存在的一般投影神经网络的改进版本，能够解决EGVI问题。此外，数值例子的仿真结果表明了所提出神经网络的有效性和性能。
