Diagonal recurrent neural network for controller designs

Abstract

A new neural network paradigm called diagonal recurrent neural network (DRNN) structure is presented, and is used to design a neural network controller, which includes both a neuroidentifier (DRNI) and a neurocontroller (DRNC). An unknown plant is identified by a neuroidentifier, which provides the sensitivity information of the plant to a neurocontroller. A generalized dynamical backpropagation algorithm (DBP) is developed to train both DRNC and DRNI. An approach to use an adaptive learning rate scheme based on the Lyapunov function is developed. The use of adaptive learning rates not only accelerates the learning speed but also guarantees the convergence of the neural network.<>