Identification of Chemical Processes using Recurrent Networks

Abstract

Neurl networks have been widely used in many research areas including nonlinear system identification. In the present study, a recurrent neural network, as an alternative to feed-forward networks, has been used successfully to identify the dynamic behavior of a biological wastewater treatment plant. An approach to deriving the learning algorithm for recurrent networks is discussed. In comparison to a feed-forward network, the recurrent network produces superior results for long-term predictions.