Sensor drift compensation algorithm based on PDF distance minimization

In this paper, a new unsupervised classification algorithm is introduced for the compensation of sensor drift effects of the odor sensing system using a conducting polymer sensor array. The proposed method continues updating adaptive Radial Basis Function Network (RBFN) weights in the testing phase based on minimizing Euclidian Distance between two Probability Density Functions (PDFs) of a set of training phase output data and another set of testing phase output data. The output in the testing phase using the fixed weights of the RBFN are significantly dispersed and shifted from each target value due mostly to sensor drift effect. In the experimental results, the output data by the proposed methods are observed to be concentrated closer again to their own target values significantly. This indicates that the proposed method can be effectively applied to improved odor sensing system equipped with the capability of sensor drift effect compensation.