title: Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model.
abstract: A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staphylococcu aureus, Escherichia coli and Pseudomonas aeruginosa) in this experiment. In addition, KELM is compared with five existing classification methods: Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), extreme learning machine (ELM), k-nearest neighbor (KNN) and support vector machine (SVM). Meanwhile, three traditional optimization methods including particle swarm optimization algorithm (PSO), genetic algorithm (GA) and grid search algorithm (GS) and four kernel functions (Gaussian kernel, linear kernel, polynomial kernel and wavelet kernel) for KELM are discussed in this experiment. Finally, the QPSO-KELM model is also used to deal with another two experimental E-nose datasets in the previous experiments. The experimental results demonstrate the superiority of QPSO-KELM in various E-nose applications.
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. pengchaocg@163.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Peng
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. yanjia119@swu.edu.cn.
authors.forename: Jia
authors.initials: J
authors.lastname: Yan
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. duansk@swu.edu.cn.
authors.forename: Shukai
authors.initials: S
authors.lastname: Duan
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. ldwang@swu.edu.cn.
authors.forename: Lidan
authors.initials: L
authors.lastname: Wang
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. jiapengfei@swu.edu.cn.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Jia
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. z574066616@163.com.
authors.forename: Songlin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001419:Bacteria / Q000302:isolation & purification*; D015374:Biosensing Techniques / Q000379:methods*; D062609:Electronic Nose; D004926:Escherichia coli / Q000302:isolation & purification; D011550:Pseudomonas aeruginosa / Q000302:isolation & purification; D013211:Staphylococcus aureus / Q000302:isolation & purification; D060388:Support Vector Machine
keywords: electronic nose; feature extraction; kernel extreme learning machine; quantum-behaved particle swarm optimization