M. Nikravesh et al., MODEL IDENTIFICATION OF NONLINEAR TIME-VARIANT PROCESSES VIA ARTIFICIAL NEURAL-NETWORK, Computers & chemical engineering, 20(11), 1996, pp. 1277-1290
This paper demonstrates that neural networks in conjunction with recur
sive least squares can be used effectively for model identification of
nonlinear time variant processes. The developed approach updates the
process model partially at any given sampling time. By updating only a
subset of parameters at a given time sample, rather than all network
parameters, convergence time is significantly reduced. In addition, me
eting the convergence criteria and over-parametrization are less of a
problem. The updating approach is applied to a nonisothermal CSTR with
time varying parameters and its performance is demonstrated. The resu
lting approach predicts the process output extremely well and has the
ability to learn on-line. Copyright (C) 1996 Elsevier Science Ltd