Using neural networks and genetic algorithms to enhance performance in an electronic nose |
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Authors: | Kermani B G Schiffman S S Nagle H T |
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Institution: | Department of Electrical and Computer Engineering, North Carolina State University, Raleigh 27695-7911, USA. bahram@lucent.com |
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Abstract: | Sensitivity, repeatability, and discernment are three major issues in any classification problem. In this study, an electronic nose with an array of 32 sensors was used to classify a range of odorous substances. The collective time response of the sensor array was first partitioned into four time segments, using four smooth time-windowing functions. The dimension of the data associated with each time segment was then reduced by applying the Karhunen-Loéve (truncated) expansion (KLE). An ensemble of the reduced data patterns was then used to train a neural network (NN) using the Levenberg-Marquardt (LM) learning method. A genetic algorithm (GA)-based evolutionary computation method was used to devise the appropriate NN training parameters, as well as the effective database partitions/features. Finally, it was shown that a GA-supervised NN system (GANN) outperforms the NN-only classifier, for the classes of the odorants investigated in this study (fragrances, hog farm air, and soft beverages). |
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