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

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; in revised form: 17 December 2012 / Accepted: 19 December 2012 / Published: 24 December 2012
PDF Full-text Download PDF Full-Text [852 KB, uploaded 24 December 2012 09:25 CET]
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.
Keywords: analog MLP circuit; electronic nose

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

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.

AMA Style

Pan C-H, Hsieh H-Y, Tang K-T. An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose. Sensors. 2013; 13(1):193-207.

Chicago/Turabian Style

Pan, Chih-Heng; Hsieh, Hung-Yi; Tang, Kea-Tiong. 2013. "An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose." Sensors 13, no. 1: 193-207.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert