DocumentCode
3314927
Title
System identification using neural networks
Author
Sugimoto, S. ; Matsumoto, M. ; Kabe, M.
Author_Institution
Dept. of Electr. Eng., Ritsumeikan Univ., Kyoto, Japan
fYear
1992
fDate
17-19 Sep 1992
Firstpage
79
Lastpage
82
Abstract
An identification problem for multivariable discrete-time linear systems is considered in the framework of neural networks and computing. A parallel computing algorithm for identification for discrete-time linear systems based on the modified Hopfield model with continuous states is proposed. The proposed parallel algorithm inherently can solve a wide variety of least-squares problems. Simulation results are presented
Keywords
discrete time systems; identification; least squares approximations; linear systems; multivariable systems; parallel algorithms; identification; least-squares problems; modified Hopfield model; multivariable discrete-time linear systems; neural networks; parallel computing algorithm; Neural networks; Neurons; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems Engineering, 1992., IEEE International Conference on
Conference_Location
Kobe
Print_ISBN
0-7803-0734-8
Type
conf
DOI
10.1109/ICSYSE.1992.236938
Filename
236938
Link To Document