 Application of T-S fuzzy neural network based on declination compensation in soft sensingYing Zhang Mathematics and Computers in Simulation (MATCOM), 2012, vol. 86, issue C, 92-99 Abstract: Soft sensing can be used in the case of watching the variables which are difficult or unable to be measured, or can be measured only with a high cost and significant delays. The key problem for soft sensing is that the method of system identification should meet the accuracy requirement of the real system. An improved T-S fuzzy neural network based on declination compensation is proposed in this paper, which increases the accuracy of system identification by constructing networks of declination compensation. The input of the samples is regarded as the input of the corrected network, the system declinations are regarded as the output samples of the corrected network, the output variables can be compensated by the output of this corrected system dynamically. The testing in catalytic cracking processes demonstrates that the improved T-S fuzzy neural network achieves better results in soft sensing compared with the original network. Keywords: Soft sensing; System identification; T-S fuzzy neural network; Declination compensation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:86:y:2012:i:c:p:92-99 DOI: 10.1016/j.matcom.2010.12.032 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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