Diagonal recurrent neural networks for nonlinear system control

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.<>