Next Article in Journal
Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering
Next Article in Special Issue
Electronic Nose Testing Procedure for the Definition of Minimum Performance Requirements for Environmental Odor Monitoring
Previous Article in Journal
Regular Deployment of Wireless Sensors to Achieve Connectivity and Information Coverage
Previous Article in Special Issue
Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1275; doi:10.3390/s16081275

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Academic Editor: Carmen Horrillo Güemes
Received: 27 April 2016 / Revised: 6 August 2016 / Accepted: 9 August 2016 / Published: 12 August 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
View Full-Text   |   Download PDF [2170 KB, uploaded 12 August 2016]   |  

Abstract

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas. View Full-Text
Keywords: EKH; electronic nose; optimization algorithm; decision weighting factor; indoor pollutant gas EKH; electronic nose; optimization algorithm; decision weighting factor; indoor pollutant gas
Figures

Figure 1

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, L.; Jia, P.; Huang, T.; Duan, S.; Yan, J.; Wang, L. A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm. Sensors 2016, 16, 1275.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top