NEURAL NETWORK MODELLING FOR A DYNAMIC PROCESS PLANT IN PROCESS CONTROL

Presents a new dynamic system identification method based on the operating points of the process plant. A feedforward neural network is trained using filtered inputs and outputs to identify the plant parameters at the operating points. The parameters are then used by a linear dynamic model to predict the future process output. The proposed identification algorithm was analyzed in detail. Experiments were performed and the results were compared with existing dynamic neural network model, showing that the new method is fast and accurate, and is particularly useful in noisy nonlinear identification.