基于回归神经网络的非线性动态数据校核及其应用

Nonlinear Dynamic Data Reconciliation Based on Recurrent Neural Networks and its Application

研究了简化型内回归神经网络基于自适应梯度下降法的训练算法 ,并提出了一种基于简化型内回归神经网络的非线性动态数据校核新方法。结果表明所提出的方法能够有效地对非线性动态过程进行数据校核 ,并具有良好性能 ;与传统的动态数据校核方法相比 ,所提出方法具有不需要掌握过程本身的精确模型 ,避免了过程模型误差可能带来的估计误差 ,不需事先知道测量噪声和过程噪声的统计特性等特点。

An adaptive gradient descent algorithm for training simplified internally recurrent networks (SIRN) is developed and a new method of reconciling nonlinear dynamic data based on SIRN is proposed. It can reconcile measurements of nonlinear dynamic process and dispenses with the need for accurate process model and prior information about statistical characteristics of noises involved. Simulation research and its application in a continually stirred tank reactor has demonstrated its fairly good performance.