title: An analog multilayer perceptron neural network for a portable electronic nose.
abstract: This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.
authors.affiliation: Department of Electrical Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, 30013 Hsinchu, Taiwan. u931814@oz.nthu.edu.tw
authors.forename: Chih-Heng
authors.initials: CH
authors.lastname: Pan
authors.forename: Hung-Yi
authors.initials: HY
authors.lastname: Hsieh
authors.forename: Kea-Tiong
authors.initials: KT
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D062609:Electronic Nose; D005638:Fruit; D016571:Neural Networks, Computer; D009812:Odorants / Q000032:analysis