DocumentCode
2207904
Title
Modeling of non-stationary process by modular separation of stability and plasticity
Author
Lampinen, Jouko
Author_Institution
Lab. of Comput. Eng., Helsinki Univ. of Technol., Espoo, Finland
Volume
1
fYear
1998
fDate
4-8 May 1998
Firstpage
199
Abstract
This paper presents a method for modeling a non-stationary process by a combination of fast learning and slowly learning modules, where the fast learning modules transform the input and output data for stable kernel module, which models a situation normalized to be stationary. The proposed method is applied to modeling a non-stationary chemical process
Keywords
backpropagation; chemical industry; learning (artificial intelligence); multilayer perceptrons; process control; stability; backpropagation; chemical process; kernel model; learning modules; modular separation; multilayer perceptrons; nonstationary process modelling; stability; Adaptive control; Chemical processes; Data engineering; Intelligent robots; Kernel; Laboratories; Multilayer perceptrons; Programmable control; Radial basis function networks; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.682262
Filename
682262
Link To Document