Using neural networks and genetic algorithms to enhance performance in an electronic nose.
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).
Kermani
B G
BG
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh 27695-7911, USA. bahram@lucent.com
Schiffman
S S
SS
Nagle
H T
HT
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