A combinative function approximation model and its applications to electronic noses

This paper focuses on combinative and modular approximation models to simultaneously estimate odor classes and strengths. We first decompose a many-to-many approximation task into multiple many-to-one tasks, and then realize them using multiple many-to-one approximation models. A single model is regarded as an expert, and a panel or ensemble is made up of multiple such experts. Each expert is either a multivariate logarithmic regression model, or a multilayer perceptron (MLP), or a support vector machine (SVM). A panel is on behalf of a kind of odor. The most similar panel gives the class label and strength of an odor. The experiment for estimating 4 kinds of fragrant materials shows that the proposed model is effective.