کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
740404 1462108 2013 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Chaotic time series prediction of E-nose sensor drift in embedded phase space
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Chaotic time series prediction of E-nose sensor drift in embedded phase space
چکیده انگلیسی

Chemical sensor drift shows a chaotic behavior and unpredictability in long-term observation which makes it difficult to construct an appropriate sensor drift treatment. The main purpose of this paper is to study a new methodology for chaotic time series modeling of chemical sensor observations in embedded phase space. This method realizes a long-term prediction of sensor baseline and drift based on phase space reconstruction (PSR) and radial basis function (RBF) neural network. PSR can memory all of the properties of a chaotic attractor and clearly show the motion trace of a time series, thus PSR makes the long-term drift prediction using RBF neural network possible. Experimental observation data of three metal oxide semiconductor sensors in a year demonstrate the obvious chaotic behavior through the Lyapunov exponents. Results demonstrate that the proposed model can make long-term and accurate prediction of chemical sensor baseline and drift time series.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sensors and Actuators B: Chemical - Volume 182, June 2013, Pages 71–79
نویسندگان
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