Drift Compensation for E-Nose Using QPSO-Based Domain Adaptation Kernel ELM

Yulin Jian
Yulin Jian
Changjian Deng
Changjian Deng
Tailai Wen
Tailai Wen

ADVANCES IN NEURAL NETWORKS - ISNN 2018, pp. 148-156, 2018.

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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 clas...More

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