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• W2100024921 abstract "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." @default.
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• W2100024921 date "2013-06-01" @default.
• W2100024921 modified "2023-10-14" @default.
• W2100024921 title "Chaotic time series prediction of E-nose sensor drift in embedded phase space" @default.
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• W2100024921 doi "https://doi.org/10.1016/j.snb.2013.03.003" @default.
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