Sensor drift compensation using weighted neural networks

In gas classification systems with multiple sensors, the individual sensor drift affects the system classification capacity over time. A model created to classify data at certain time, doesn't present the same efficiency to classify a sample in a future time. Depending on the problem, this time interval can be days, weeks or months. Chemical gas sensors suffer from drift problem because of the chemical process employed. In this investigation we developed a model that uses an ensemble of neural networks in a parallel way combining the weighted output of classifiers to compensate the drift. Another approach was to weight input data according to their recentness by repeating newer training values. Results show that performance of correct classifications of the gas samples using both methods improved when compared to classifiers trained with just recent data.