title: Using neural networks and genetic algorithms to enhance performance in an electronic nose.
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).
authors.affiliation: Department of Electrical and Computer Engineering, North Carolina State University, Raleigh 27695-7911, USA. bahram@lucent.com
authors.forename: B G
authors.initials: BG
authors.lastname: Kermani
authors.forename: S S
authors.initials: SS
authors.lastname: Schiffman
authors.forename: H T
authors.initials: HT
authors.lastname: Nagle
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D008954:Models, Biological; D016571:Neural Networks, Computer; D009812:Odorants / Q000032:analysis*; D010363:Pattern Recognition, Automated; D011897:Random Allocation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012903:Smell / Q000235:genetics*; D012985:Software Design