Gray layer technology: incorporating a priori knowledge into feedforward artificial neural networks

Gray layer technology represents a novel method for incorporating prior information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network (ANN). A prescriptive technique is developed that sets or constrains the weights of a particular layer, designated the gray layer, according to a known or partially known function that is related in some manner to the system of interest. An algorithm is derived for backpropagating the error through the gray layer and adjusting the parameters of the gray layer that is consistent with other ANN learning techniques. Gray layer technology is applied to the identification of two different partially known dynamic systems. Results demonstrate a significant improvement in the quality of the identification model and an increase in the rate of convergence