论文标题
开关系统的基于神经网络的深度自适应学习
Deep neural network based adaptive learning for switched systems
论文作者
论文摘要
在本文中，我们为开关系统提出了一种基于神经网络的自适应学习（DNN-AL）方法。当前，基于Deep神经网络的方法是为了学习未知动态系统中的方程式而积极开发的，但是它们的效率可能会因离散时间时存在结构性变化而对开关系统退化。在这种新的DNN-AL策略中，观察到的数据集被自适应分解为子集，因此每个子集中没有结构性变化。在自适应过程中，DNN是层次结构的，并且逐渐识别出未知的切换时间。尤其是，重新使用先前迭代步骤的网络参数以初始化后期迭代步骤的网络，从而为DNN提供有效的培训程序。对于通过我们的DNN-AL获得的DNN，建立了预测误差的界限。进行了数值研究以证明DNN-AL的效率。
In this paper, we present a deep neural network based adaptive learning (DNN-AL) approach for switched systems. Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems, but their efficiency can degenerate for switching systems, where structural changes exist at discrete time instants. In this new DNN-AL strategy, observed datasets are adaptively decomposed into subsets, such that no structural changes within each subset. During the adaptive procedures, DNNs are hierarchically constructed, and unknown switching time instants are gradually identified. Especially, network parameters at previous iteration steps are reused to initialize networks for the later iteration steps, which gives efficient training procedures for the DNNs. For the DNNs obtained through our DNN-AL, bounds of the prediction error are established. Numerical studies are conducted to demonstrate the efficiency of DNN-AL.
