A statistical perspective of neural networks for process modeling and control

Author

Qin, S. Joe

Author_Institution

Fisher-Rosemount Syst., Austin, TX, USA

fYear

1993

fDate

25-27 Aug 1993

Firstpage

599

Lastpage

604

Abstract

Multilayer neural networks have been successfully applied to industrial process modeling and control. The prediction variance of neural networks from gradient based learning is analyzed in the presence of correlated process inputs. Several biased regression approaches, including ridge regression, principal component analysis, and partial least squares, are integrated with neural net training to reduce the prediction variance. Examples are given to illustrate the improvement of the integrated approaches

Keywords

feedforward neural nets; learning (artificial intelligence); process control; statistical analysis; biased regression; gradient based learning; industrial process modeling; multilayer neural network; partial least squares; prediction variance; principal component analysis; process control; ridge regression; Artificial neural networks; Chemical processes; Least squares approximation; Least squares methods; Multi-layer neural network; Neural networks; Noise measurement; Principal component analysis; Process control; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 1993., Proceedings of the 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

ISSN

2158-9860

Print_ISBN

0-7803-1206-6

Type

conf

DOI

10.1109/ISIC.1993.397629

Filename

397629