论文标题
使用闭环数据的神经网络培训：危害和仪器变量（IVNN）解决方案
Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution
论文作者
论文摘要
正在观察到神经网络在控制系统中的使用趋势越来越大。本文的目的是揭示使用闭环数据的学习神经网络进料控制器的直接应用可能会引入参数不一致，从而降低控制性能并提供解决方案。所提出的方法采用仪器变量来确保一致的参数估计值。一个非线性系统示例表明，开发的仪器可变神经网络（IVNN）方法渐近地恢复了最佳解决方案，而预先存在的方法则显示导致不一致的估计值。
An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural network feedforward controllers with closed-loop data may introduce parameter inconsistency that degrades control performance, and to provide a solution. The proposed method employs instrumental variables to ensure consistent parameter estimates. A nonlinear system example reveals that the developed instrumental variable neural network (IVNN) approach asymptotically recovers the optimal solution, while pre-existing approaches are shown to lead to inconsistent estimates.
