DYNAMIC PROCESS MODELING WITH RECURRENT NEURAL NETWORKS

A method of nonlinear static and dynamic process modeling via recurrent neural networks (RNNs) is studied. An RNN model is a set of coupled nonlinear ordinary differential equations in continuous time domain with nonlinear dynamic node characteristics as well as both feedforward and feedback connections. For such networks, each physical input to a system corresponds to exactly one input to the network. The system's dynamics are captured by the internal structure of the network. The structure of RNN models may be more natural and attractive than that of feedforward neural network models, but computation time for training is longer. Our simulation results show that RNNs can learn both steady-state relationships and process dynamics of continuous and batch, single-input/single-output and multi-input/multioutput systems in a simple and direct manner. Training of RNNs shows only small degradation in the presence of noise in the training data. Thus, RNNs constitute a feasible alternative to layered feedforward backpropagation neural networks in steady-state and dynamic process modeling and model-based control.