Hybrid neural network predictor for distributed parameter system based on nonlinear dimension reduction.

In this study, a hybrid neural network predictor is proposed to predict spatiotemporal dynamics of the nonlinear distributed parameter systems (DPSs) with unwanted disturbance or slow set point changes. First, a nonlinear principal component analysis (NL-PCA) network is designed to transform the high-dimensional spatiotemporal data into a low-dimensional time domain, which can better represent the nonlinearity of the system compared to the linear time/space separation method. Then the hybrid NN models are built to identify the low-dimensional temporal data. To capture the spatiotemporal dynamics of DPS, the four-step recursive algorithm is used to obtain the time-varying weights of the model, while the parameters of NN model does not need to online update. The simulations demonstrated show that the proposed approach can achieve a good performance on prediction with system slow time-varying dynamics. [ABSTRACT FROM AUTHOR]