On identification of partially known dynamic nonlinear systems with neural network

A method for incorporating a priori information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network is presented. The result is an improved identification model and controller structures suitable for nonlinear system identification and control applications. The known information is incorporated into the activation function of the network output layer. An algorithm is derived for backpropagating the error and updating adjustable parameters within this layer that is consistent with existing supervised learning techniques. The developed technique is applied to the identification of several dynamic systems and compared with existing approaches. The results exhibit a significant improvement in the quality of the identification model and an increase in the rate of convergence. The results further demonstrate that artificial neural networks using a priori information converge faster and more accurately predict the system of interest