DocumentCode
1663365
Title
Modelling of direction-dependent dynamic processes: a comparison of Wiener models and neural networks
Author
Tan, A.H. ; Godfrey, K.R.
Author_Institution
Div. of Electr. & Electron. Eng., Warwick Univ., Coventry, UK
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
215
Abstract
The modelling of direction-dependent processes using Wiener and neural network models is compared for several different processes and for three different types of input signal: a pseudorandom binary signal (prbs), an inverse-repeat pseudo-random binary signal (irprbs) and a multisine (sum of harmonics) signal. Experimental results on an electronic nose are presented to illustrate the applicability of the techniques discussed.
Keywords
chemioception; dynamic response; gas sensors; harmonics; modelling; neural nets; stochastic processes; Wiener models; direction-dependent dynamic processes; electronic nose; input signal; inverse-repeat pseudo-random binary signal; modelling; multisine signal; neural networks; pseudo-random binary signal; sum of harmonics signal; Chemical industry; Delay effects; Electronic noses; Neural networks; Pattern matching; Signal analysis; Signal processing; Time factors; Transfer functions; Vehicle dynamics;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation and Measurement Technology Conference, 2002. IMTC/2002. Proceedings of the 19th IEEE
ISSN
1091-5281
Print_ISBN
0-7803-7218-2
Type
conf
DOI
10.1109/IMTC.2002.1006842
Filename
1006842
Link To Document