Title :
The identification of nonlinear dynamic systems around operating points using neural networks
Author :
Pienaar, J.D. ; Bodenstein, C.P.
Author_Institution :
Sch. of Electr. & Electron. Eng., Potchefstroom Univ. for CHE, South Africa
Abstract :
This paper discusses a method of modeling the dynamic relationships of a nonlinear time-invariant or slowly varying system, typical of those found in the petrochemical industry. A number of setpoints are identified, and linear models are constructed around these points. Every linear model is then used to construct a NARMAX model (nonlinear autoregressive moving average model with autogeneous inputs). Finally a chemical process is modeled to illustrate the concepts illustrated in this paper
Keywords :
autoregressive moving average processes; chemical industry; identification; neural nets; nonlinear dynamical systems; NARMAX model; autogeneous inputs; chemical process modeling; linear models; neural networks; nonlinear autoregressive moving average model; nonlinear dynamic systems identification; nonlinear time-invariant system; operating points; slowly varying system; Africa; Autoregressive processes; Chemical industry; Control system synthesis; Electrical equipment industry; Neural networks; Nonlinear dynamical systems; Petrochemicals; Predictive models; System identification;
Conference_Titel :
Industrial Electronics, 1998. Proceedings. ISIE '98. IEEE International Symposium on
Conference_Location :
Pretoria
Print_ISBN :
0-7803-4756-0
DOI :
10.1109/ISIE.1998.707757
