论文标题
单变量恢复神经网络及其在非线性系统识别中的应用
Univariate ReLU neural network and its application in nonlinear system identification
论文作者
论文摘要
自从出现以来，Relu（整流线性单元）神经网络已受到了极大的关注。在本文中，提出了一个单变量的神经网络来建模非线性动态系统和揭示有关该系统的见解。具体而言，神经网络由具有线性和尿素激活函数的神经元组成，尿素函数定义为相对于每个维度的依赖函数。 Urelu神经网络是一个隐藏的层神经网络，结构相对简单。神经网络的初始化采用了脱钩方法，该方法提供了良好的初始化和对非线性系统的一些见解。与正常的Relu神经网络相比，尿路网络的参数数量较小，但它仍然提供了非线性动态系统的良好近似值。尿路神经网络的性能通过滞后基准系统显示：Bouc-Wen系统。仿真结果验证了所提出的方法的有效性。
ReLU (rectified linear units) neural network has received significant attention since its emergence. In this paper, a univariate ReLU (UReLU) neural network is proposed to both modelling the nonlinear dynamic system and revealing insights about the system. Specifically, the neural network consists of neurons with linear and UReLU activation functions, and the UReLU functions are defined as the ReLU functions respect to each dimension. The UReLU neural network is a single hidden layer neural network, and the structure is relatively simple. The initialization of the neural network employs the decoupling method, which provides a good initialization and some insight into the nonlinear system. Compared with normal ReLU neural network, the number of parameters of UReLU network is less, but it still provide a good approximation of the nonlinear dynamic system. The performance of the UReLU neural network is shown through a Hysteretic benchmark system: the Bouc-Wen system. Simulation results verify the effectiveness of the proposed method.
