Title :
RBFN identification of a solution copolymerization model
Author :
Bomberger, John D. ; Seborg, Dale E. ; Ogunnaike, Babatunde A.
Author_Institution :
Dept. of Chem. Eng., California Univ., Santa Barbara, CA, USA
Abstract :
Methods developed for radial basis function network (RBFN) identification are applied to a complex multiple-input, multiple-output (MIMO) simulation. For RBFN identification, stepwise regression analysis is used, together with model order determination using the method of false nearest neighbors and width parameter estimation using approximate gradient norms. Industrially practical input sequence design is also considered
Keywords :
MIMO systems; autoregressive processes; chemical technology; feedforward neural nets; parameter estimation; polymerisation; process control; statistical analysis; MIMO simulation; RBFN; approximate gradient norms; complex multiple-input multiple-output simulation; false nearest neighbors method; input sequence design; model order determination; radial basis function network identification; solution copolymerization model; stepwise regression analysis; width parameter estimation; Chemical engineering; Continuous-stirred tank reactor; Input variables; MIMO; Nearest neighbor searches; Parameter estimation; Polymers; Radial basis function networks; Regression analysis; Solvents;
Conference_Titel :
American Control Conference, 1998. Proceedings of the 1998
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-4530-4
DOI :
10.1109/ACC.1998.688449
