Inverting recurrent neural networks for internal model control of nonlinear systems
C. Kambhampati, R. Craddock, M. Tham, Kevin Warwick · 1998
In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.