Han Zhimin, Li Shenggang, Liu Heng
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China.
School of Science, Guangxi University for Nationalities, Nanning 530006, China.
J Adv Res. 2020 Apr 26;25:87-96. doi: 10.1016/j.jare.2020.04.006. eCollection 2020 Sep.
In this work, a sliding mode control (SMC) method and a composite learning SMC (CLSMC) method are proposed to solve the synchronization problem of chaotic fractional-order neural networks (FONNs). A sliding mode surface and an adaptive law are constructed to update parameter estimation. The SMC ensures that the synchronization error asymptotically tends to zero under a strict permanent excitation (PE) condition. To reduce its rigor, online recording data together with instantaneous data is used to define a prediction error about the uncertain parameter. Both synchronization error and prediction error are used to construct a composite learning law. The proposed CLSMC method can ensure that the synchronization error asymptotically approaches zero, and it can accurately estimate the uncertain parameter. The above results obtained in the CLSMC method only requires an interval-excitation (IE) condition which can be easily satisfied. Finally, comparative results reveal the control effects of the two proposed methods.
在这项工作中，提出了一种滑模控制（SMC）方法和一种复合学习滑模控制（CLSMC）方法来解决混沌分数阶神经网络（FONN）的同步问题。构造了一个滑模面和一个自适应律来更新参数估计。滑模控制确保在严格的持续激励（PE）条件下同步误差渐近趋于零。为了降低其严格性，使用在线记录数据和瞬时数据来定义关于不确定参数的预测误差。同步误差和预测误差都用于构建复合学习律。所提出的CLSMC方法可以确保同步误差渐近趋近于零，并且可以准确估计不确定参数。CLSMC方法中获得的上述结果仅需要一个易于满足的区间激励（IE）条件。最后，比较结果揭示了所提出的两种方法的控制效果。
