A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization. Issue 3 (10th June 2014)

• Record Type: Journal Article
• Authors: Jia, Pengfei; Tian, Fengchun; Fan, Shu; He, Qinghua; Feng, Jingwei; X. Yang, Simon
• Is Part Of: Sensor review. Volume 34:Issue 3(2014)
• Page Start: 304
• Page End: 311
• Publication Date: 2014-06-10
• Subjects: Signal processing -- Sensors -- Surgery; Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-02-2013-630
• Languages: English
• ISSNs: 0260-2288

Abstract

Abstract : Purpose: – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array's optimization and parameters' setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach: – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings: – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier's parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications: – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications: – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value: – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.