Chaotic Time Series-Based Sensor Drift Prediction

Chemical sensor drift shows a chaotic behavior and unpredictability in long-term observation, such that constructing an appropriate sensor drift treatment is difficult. This chapter introduces 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 become possible. Experimental observation data of three metal oxide semiconductor sensors in a year demonstrates the obvious chaotic behavior through the Lyapunov exponents. Results demonstrate that the proposed model can make long-term and accurate prediction of time series chemical sensor baseline and drift.