Modular neural networks for estimating odor concentrations

The concentration estimation for multiple kinds of odors is regarded first as multiple two-class classification and then as multiple approximation problems, and solved by multiple single-output multi-layer perceptrons (MLPs) lined up in two parallel rows. A pair of MLPs in cascade is on behalf of a specified odor. n pairs of MLPs represent n kinds of odors, one for one. An MLP in the first row separates its represented odor from the others. Because the two-class training subsets are often unbalanced, the samples from the minority sides are virtually reinforced. The generalization of an MLP is limited in local regions with respect to the distribution of the represented odor. An MLP in the second row approximates the relationship between the responses of the sensor array and the concentrations of the represented odor. A sample is assigned to a kind of odor by the MLP with the maximum output in the first row, and then its concentration is estimated by another MLP in the corresponding pair. The effectiveness of the proposed MLP models is verified by the experiments for 4 kinds of fragrant materials as well as their extended dataset.