Diagonal recurrent neural networks for nonlinear system control

Abstract

The authors present an approach for control and system identification using diagonal recurrent neural networks (DRNNs). An unknown plant is identified by a system identifier, called a diagonal recurrent neuroidentifier (DRNI), and provides information on the plant to a controller, called a diagonal recurrent neurocontroller (DRNC). A generalized algorithm, called the dynamic backpropagation algorithm, is developed to train both the DRNC and the DRNI. The DRNN captures the dynamic nature of a system and, since it is not fully connected, training is much faster than with a fully connected recurrent neural network.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>