Hu Xiaolin, Wang Jun
Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
IEEE Trans Syst Man Cybern B Cybern. 2007 Jun;37(3):528-39. doi: 10.1109/tsmcb.2006.886166.
This paper presents a recurrent neural-network model for solving a special class of general variational inequalities (GVIs), which includes classical VIs as special cases. It is proved that the proposed neural network (NN) for solving this class of GVIs can be globally convergent, globally asymptotically stable, and globally exponentially stable under different conditions. The proposed NN can be viewed as a modified version of the general projection NN existing in the literature. Several numerical examples are provided to demonstrate the effectiveness and performance of the proposed NN.
本文提出了一种用于求解一类特殊的广义变分不等式（GVIs）的递归神经网络模型，其中经典变分不等式作为特殊情况包含在内。结果表明，所提出的用于求解此类GVIs的神经网络（NN）在不同条件下可以是全局收敛的、全局渐近稳定的和全局指数稳定的。所提出的NN可以看作是文献中现有一般投影NN的改进版本。提供了几个数值例子来证明所提出的NN的有效性和性能。
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