Simultaneous estimation of odor classes and concentrations using an electronic nose

This paper sets up an electronic nose, and presents a kind of combinative and modular single-hidden-layer perceptrons. Every module is made up of multiple single-input single-output multilayer perceptrons (MLPs). One MLP is regarded as an expert, and one module consists of several such experts. In electronic noses, one module is behalf of a kind of odor, and determines its similar degrees, namely its strengths. The most similar module gives the class and strength of the odor. By means of enlarging the input components to the range of [0, 6.0] and transforming the standard sigmoid activation function to be f(x)=3(1+exp(-x/3))/sup -1/, the learning speeds of MLPs are sped up. The experiment for simultaneously estimating the classes and concentrations of 4 kinds of fragrant materials, namely ethanol, ethyl acetate, ethyl caproate and ethyl lactate in different concentrations, shows that the proposed method is quite effective.