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Sensors 2017, 17(6), 1434; doi:10.3390/s17061434

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

1
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China
3
High Tech Department, China International Engineering Consulting Corporation, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Received: 18 April 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 19 June 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
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Abstract

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. View Full-Text
Keywords: electronic nose; gas classification; extreme learning machine; multiple kernel learning; weighted kernels; parameter optimization electronic nose; gas classification; extreme learning machine; multiple kernel learning; weighted kernels; parameter optimization
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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).

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Jian, Y.; Huang, D.; Yan, J.; Lu, K.; Huang, Y.; Wen, T.; Zeng, T.; Zhong, S.; Xie, Q. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach. Sensors 2017, 17, 1434.

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