Dynamic partial least squares modeling with recurrent neural networks of stable learning
带有稳定学习的递归神经网络动态偏最小二乘建模

A dynamic modeling algorithm is proposed for a strongly nonlinear chemical process, it is based on partial least squares (PLS) and recurrent neural networks with a stable learning rate. The outer PLS algorithm reduces the dimensionality of data and extracts score vector, and the inner model which combines recurrent neural networks with Hammerstein model captures the nonlinear characters to extend the model application scope. Besides, the stable learning algorithm updates the model parameters to improve the prediction precision and adaptation ability. This method is implemented in the process of alumina production to measure the component concentrations of sodium aluminate solution. Simulation results show that the modeling method is effective.