Abstract
Purpose - The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low cost e-noses with metal oxide semiconductor sensors in indoor air contaminant monitoring, and overcome the potential sensor drift.Design/methodology/approach - In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with embedded self-calibration module based on a threshold network is studied.Findings - The nonlinear estimation problem of sensor array based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.Originality/value - A number of experimental results have been presented in this paper, and also demonstrate that the proposed model has very good accuracy and robustness in real time indoor monitoring of formaldehyd.
Purpose - The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low cost e-noses with metal oxide semiconductor sensors in indoor air contaminant monitoring, and overcome the potential sensor drift.Design/methodology/approach - In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with embedded self-calibration module based on a threshold network is studied.Findings - The nonlinear estimation problem of sensor array based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.Originality/value - A number of experimental results have been presented in this paper, and also demonstrate that the proposed model has very good accuracy and robustness in real time indoor monitoring of formaldehyd.