RESEARCH INITIATION AWARD: The Application of Neural Networks to Gas Sensors

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9309012 Segee Sensor technology exist that is capable of detecting potentially dangerous gases even in very low concentrations. However, these sensors are not very selective and often have responses that are nonlinear function of gas concentration. Selectivity has been improved by using an array of sensors having differing sensitivities. The mathematics of calibrating such an array is very complex and poor performance and even instability can result if the responses of individual sensors are too similar. If its generally necessary to disregard valid sensor data from a subset of the available sensor. To address the above problem artificial neural network models have been suggested as an alternative. They are capable of learning multidimensional nonlinear functions, can exploit redundant information and are adaptable. The proposed work will use data gathered form sensors in a controlled environment to train three different types of artificial neural networks to identify gas type and concentration. Performance of these neural networks will be measured first by means of a test set gathered at the same time as the training data, but not used for training. Ultimately, performance in situ will be measured and on-line training used to fine tune performance. ***