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Resolving problems in closed loop nonlinear process identification using IRN
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DOI：10.1016/0098-1354(95)00243-X.png)
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The feasibility of identifying a nonlinear dynamic process model by an IRN (internal recurrent neural network) using the process input and output data collected under closed loop control is considered in the paper. Conditions under which an IRN can be used for identification under closed loop control, and the problem of setting the initial conditions of an IRN, are discussed. A method to improve the accuracy of prediction of an IRN is proposed. Two simulation studies, one for a CSTR, and another for a packed distillation column, show that an IRN is a promising model for identifying dynamic nonlinear processes under closed loop control. (C) 1996 Published by Elsevier Science Ltd
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