IDENTIFICATION OF NONLINEAR DYNAMIC PROCESSES WITH UNKNOWN AND VARIABLE DEAD-TIME USING AN INTERNAL RECURRENT NEURAL-NETWORK

Methods for identifying a nonlinear dynamic process with unknown and possibly variable dead times via an internal recurrent network (IRN) model are proposed. It is shown that an IRN with sufficient hidden nodes can be used directly for the identification of a nonlinear dynamic process with fixed or variable dead times. If a process input window rather than just the current process input is used as the input to an IRN model, the number of hidden nodes in the IRN model can be reduced, and the prediction performance of the IRN improves for processes with large, and variable, dead times. Simulation results for a pH neutralization,process with transportation lags demonstrate the effectiveness of the proposed methods.