Quantization errors in committee machine for gas sensor applications

In a digital implementation of a gas identification system, the mapping of continuous real parameter values into a finite set of discrete values introduces an error into the system. This paper presents the results of an investigation into the effects of parameter quantization on different classifiers (KNN, MLP and GMM). We propose a committee machine to decrease the classification performance degradation due to the quantization errors. The simulation results show that the committee machine always outperforms a single classifier and the gain in classification performance is greater for a reduced number of bits.