Control modeling of ash wood drying using process neural networks

Abstract

For the control and system identification problems of the deceleration phase of the ash wood drying process, we propose a deceleration phase modeling method of ash wood drying using process neural networks with double hidden layers. This method applies time-varying characteristics of process neural networks and the ability to extract time-space cumulative effects. The time-varying characteristics of wood drying deceleration phase modeling under time series background are directly incorporated into the model. By comparison with traditional neural network modeling results, we prove that the model of process neural networks has better control accuracy, providing an idea to solve control and nonlinear system identification problems under a time series background.