The Library
Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure
Tools
UNSPECIFIED (2004) Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 53 (3). pp. 744-753. doi:10.1109/TIM.2004.827083 ISSN 0018-9456.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1109/TIM.2004.827083
Abstract
The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat pseudorandom binary signal and a multisine (sum of harmonics) signal. Experimental results on a real system, namely an electronic nose system, are also presented to illustrate the applicability of the techniques discussed.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||
Journal or Publication Title: | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | ||||
Publisher: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | ||||
ISSN: | 0018-9456 | ||||
Official Date: | June 2004 | ||||
Dates: |
|
||||
Volume: | 53 | ||||
Number: | 3 | ||||
Number of Pages: | 10 | ||||
Page Range: | pp. 744-753 | ||||
DOI: | 10.1109/TIM.2004.827083 | ||||
Publication Status: | Published |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |