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Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure
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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. ISSN 0018-9456
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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 |
| Date: | June 2004 |
| Volume: | 53 |
| Number: | 3 |
| Number of Pages: | 10 |
| Page Range: | pp. 744-753 |
| Identification Number: | 10.1109/TIM.2004.827083 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/8400 |
Data sourced from Thomson Reuters' Web of Knowledge
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