基于金属氧化物半导体嗅觉系统传感器阵列的混合气体识别与浓度检测算法研究。
Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array.
机构信息
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China.
School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150001, China.
出版信息
Sensors (Basel). 2018 Sep 28;18(10):3264. doi: 10.3390/s18103264.
As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH₄ as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH₄ concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.
作为一种典型的机器嗅觉系统指标，混合气体的识别和浓度检测的准确性较低。本文提出了一种新的混合气体识别和浓度检测方法。在该方法中，采用核主成分分析（KPCA）提取不同成分的非线性混合气体特征，然后采用 K 最近邻算法（KNN）分类建模实现目标气体的识别。此外，该方法采用多变量相关向量机（MVRVM）对多输入非线性信号进行回归，实现混合气体浓度的检测。该方法采用 CO 和 CH₄ 作为实验系统样本进行验证。实验结果表明，该方法的准确率达到 98.33%，分别比主成分分析（PCA）和独立成分分析（ICA）高 5.83%和 14.16%。对于混合气体浓度检测方法，CO 和 CH₄ 浓度检测的平均相对误差分别降低到 5.58%和 5.38%。
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