Wu Wenqi, Zheng Bing
IEEE Trans Neural Netw Learn Syst. 2023 Aug;34(8):4957-4965. doi: 10.1109/TNNLS.2021.3126114. Epub 2023 Aug 4.
Recently, Xu et al. solved a class of time-varying linear equations and inequalities systems (LEIESs) by using a Zhang neural network (ZNN) model through introducing a nonnegative relaxation vector. However, the introduction of this unknown nonnegative slack vector will increase the size and complexity of the model, thereby increasing the cost of computation. In this article, we propose two new ZNN models (called traditional Zhang neural network (TZNN) and variant Zhang neural network (VZNN) models, respectively) in which no additional relaxation vector is needed. The convergence analysis of these two new models are performed, and two simulation experiments are given to illustrate their efficiency and effectiveness for solving the time-varying LEIESs, including the applicability of our proposed models to robot manipulator.
最近，徐等人通过引入非负松弛向量，利用张神经网络（ZNN）模型解决了一类时变线性方程和不等式系统（LEIESs）。然而，这个未知非负松弛向量的引入会增加模型的规模和复杂性，从而增加计算成本。在本文中，我们提出了两种新的ZNN模型（分别称为传统张神经网络（TZNN）和变体张神经网络（VZNN）模型），其中不需要额外的松弛向量。对这两种新模型进行了收敛性分析，并给出了两个仿真实验来说明它们在解决时变LEIESs方面的效率和有效性，包括我们提出的模型在机器人操纵器上的适用性。
