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Sensors 2013, 13(1), 193-207; doi:10.3390/s130100193

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose

Department of Electrical Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, 30013 Hsinchu, Taiwan
* Author to whom correspondence should be addressed.
Received: 10 November 2012 / Revised: 17 December 2012 / Accepted: 19 December 2012 / Published: 24 December 2012
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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.
Keywords: analog MLP circuit; electronic nose analog MLP circuit; electronic nose
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pan, C.-H.; Hsieh, H.-Y.; Tang, K.-T. An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose. Sensors 2013, 13, 193-207.

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