Fuzzy logic applied to gas sensors

The main problem in gas sensing, using semiconductor gas sensors, is that they are not only sensitive to one gas but also to all others present in the surrounding atmosphere, i.e., the crossed-sensibility problem. A technique to approach this is to use an array of sensors. Every element is doped with different impurities. Therefore, we have not only one, but multiple outputs which will give us more information about the system. However, the lack of precision associated with the data is evident, due to the interactions between gases, and is thus very difficult to compute, but easy to be interpreted by a human expert. Fuzzy logic is extremely useful in handling vagueness, ambiguity and lack of precision inherent to data. In this logic, truth is treated as a linguistic variable that takes values from the [0, 1] interval, given by a pertinence function. Each variable has a number of linguistic labels associated with it. For example, the variable 'concentration of NOx' may have the linguistic labels, 'very high', 'high', 'medium', 'low' and 'very low', as a function of the detector's location (industrial or urban environment ...). The knowledge of the expert is stated as a set of rules for a sensor array. Applying the appropriate set of rules we shall infer the linguistic label, or the precise numeric value, which describes the output variable (in this case, the gas concentration). Therefore, we propose the use of fuzzy logic applied to an array of semiconductor gas sensors to analyse the different atmospheres due to the presence of different gases