用于求解逆混合变分不等式的近端神经动力学模型。
A proximal neurodynamic model for solving inverse mixed variational inequalities.
机构信息
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
Department of Biomedical Engineering, City University of Hong Kong, Hong Kong.
出版信息
Neural Netw. 2021 Jun;138:1-9. doi: 10.1016/j.neunet.2021.01.012. Epub 2021 Jan 27.
This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity (L-continuity). It is also shown that the equilibrium point of the proposed PNDM is asymptotically stable or exponentially stable under some mild conditions. Finally, three numerical examples are presented to illustrate effectiveness of the proposed PNDM.
本文提出了一种基于近端算子的求解逆混合变分不等式（IMVIs）的近端神经动力学模型（PNDM）。结果表明，在 Lipschitz 连续性（L-连续性）条件下，PNDM 具有唯一的连续解。还表明，在所提出的 PNDM 的平衡点在一些温和条件下是渐近稳定或指数稳定的。最后，给出了三个数值例子来说明所提出的 PNDM 的有效性。
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