Detection of Vapours and Odours from a Multisensor Array Using Pattern Recognition: Self-Organising Adaptive Resonance Techniques

where x' is the normalised response of !J sensor 1 to complex odour j over an array of n sensors. In some respects this simulates a characteristic of the human nose namely that its perception of the intensity of smells is rather poor. One disadvantage is that, in the case of weak smells the transform enhances the noise. The output from the sensor array may be regarded as an n-dimensional input vector S to the pattern recognition system where, principle and the nature of the interfering signals. It has been shown that the fractional change in conductance (Godour_ Gair)jGair helps to linearise the sensor output (with concentration) and to reduce its temperature sensitivity, thus improving the performance of chemometric and neural networking techniques• The concentration-dependence of the odour sensor can be removed in linear sensors by normalising the sensor parameter according to: intelligent signal compensation routines and pattern recognition (PARe) paradigms such as artificial neural networks to classify the output space • The current commercial instruments may be described as first generation because they possess limited capabilities to train sensor array signals and compensate for undesirable characteristics. One possible way of improving the instrument is to design signal processing techniques that more closely resemble our own well-proven olfactory system. Here we examine the implementation of one such technique, called adaptive resonance theory, which enables the electronic nose to learn new odour patterns in an on-line manner.