Abstract
A novel theoretical framework for drift compensation and classification of an electronic nose (E-nose), called QPSO-based domain adaptation kernel extreme learning machine (QDA-KELM) is presented in the work. The kernel method combines with domain adaption extreme learning machine (DAELM) to remove the drift in E-nose and enhance the classification performance. A swarm intelligent algorithm is utilized for the optimization of the model parameters. In order to evaluate the performance of our approach, three types of common kernels are used to form the composite kernel function. In addition, ELM and DAELM are compared with the proposed method. Finally, we also applied Analysis of Variance (ANOVA) to demonstrate our results are significantly better than the control methods.
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Acknowledgments
The work was supported by National Natural Science Foundation of China (Grant Nos. 61571372, 61672436), Undergraduate Students Science and Technology Innovation Fund Project of Southwest University (Grant No. 20171803005).
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Jian, Y., Lu, K., Deng, C., Wen, T., Yan, J. (2018). Drift Compensation for E-Nose Using QPSO-Based Domain Adaptation Kernel ELM. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_18
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DOI: https://doi.org/10.1007/978-3-319-92537-0_18
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