Sensors 2011, 11(5), 5005-5019; doi:10.3390/s110505005

Electronic Nose Based on an Optimized Competition Neural Network

* email, email, email, email and email
Received: 21 February 2011; in revised form: 30 March 2011 / Accepted: 29 April 2011 / Published: 4 May 2011
(This article belongs to the Section Physical Sensors)
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.
Abstract: In view of the fact that there are disadvantages in that the class number must be determined in advance, the value of learning rates are hard to fix, etc., when using traditional competitive neural networks (CNNs) in electronic noses (E-noses), an optimized CNN method was presented. The optimized CNN was established on the basis of the optimum class number of samples according to the changes of the Davies and Bouldin (DB) value and it could increase, divide, or delete neurons in order to adjust the number of neurons automatically. Moreover, the learning rate changes according to the variety of training times of each sample. The traditional CNN and the optimized CNN were applied to five kinds of sorted vinegars with an E-nose. The results showed that optimized network structures could adjust the number of clusters dynamically and resulted in good classifications.
Keywords: electronic nose; competitive neural networks; optimize
PDF Full-text Download PDF Full-Text [361 KB, uploaded 21 June 2014 03:42 CEST]

Export to BibTeX |

MDPI and ACS Style

Men, H.; Liu, H.; Pan, Y.; Wang, L.; Zhang, H. Electronic Nose Based on an Optimized Competition Neural Network. Sensors 2011, 11, 5005-5019.

AMA Style

Men H, Liu H, Pan Y, Wang L, Zhang H. Electronic Nose Based on an Optimized Competition Neural Network. Sensors. 2011; 11(5):5005-5019.

Chicago/Turabian Style

Men, Hong; Liu, Haiyan; Pan, Yunpeng; Wang, Lei; Zhang, Haiping. 2011. "Electronic Nose Based on an Optimized Competition Neural Network." Sensors 11, no. 5: 5005-5019.

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