A neural network learning algorithm of chemical process modeling based on the extended Kalman filter

Abstract

The extended Kalman filtering (EKF) algorithm instead of the error back-propagation (BP) algorithm is used to train artificial neural networks (ANNs) for chemical process modeling. The basic idea is, by modifying the EKF gain, to prevent overfitting or filtering divergence phenomenon caused by outliers in the training samples. The EKF-based ANNs training method proposed is also applied to estimate the conversion rate in the polyacrylonitrile production process. Numerical simulations show that the modified EKF algorithm is superior to the BP algorithm in resisting noise and outliers, as well as generalization.