Gas sensor array concentration detection method based on fuzzy division and model integration

Abstract

A gas sensor array concentration detection method based on fuzzy division and model integration belongs to the field of a gas sensor array signal processing technology. According to the method, time division of baseline drift data is carried out by a fuzzy clustering method, and a source dataset is divided into multiple sub-datasets with different drift degrees; then, regression models of different training datasets are established to obtain several sub-regression models; an optimal weight set of each sub-regression model is obtained within the training dataset, and center of clustering and optimal weight undergo fitting to obtain an optimal weight fitting function; and during the test phase, fitting weight is calculated on the basis of the optimal weight fitting function and clustering center time, and the sub-regression models integrate forecasted results of data to be tested so as to obtain the final gas concentration value. By the method, a pattern recognition model can be changed adaptively, changes of drifting can be traced, the influence of drifting on concentration detection performance is effectively reduced, and long-term accuracy of concentration measurement is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of gas sensor array signal processing, and in particular relates to a metal oxide gas sensor array concentration detection method based on long-term drift compensation based on fuzzy division and fuzzy weighted multi-output support vector regression model integration. Background technique [0002] The use of gas sensor devices to replace traditional analytical instruments is a new method for mixed gas detection. Metal oxide semiconductor (Metal Oxide Semiconductor, MOS) gas sensors and other types of gas sensors (such as electrochemical sensors, surface acoustic wave sensors, conductive polymer sensors, etc.) etc.) have the advantages of small size, low cost, fast response and recovery, and long service life. With the advancement of MEMS processing technology, arrays can be realized on Si substrates. Microstructure gas sensors using this process have low power consumption, good compatibility with semiconduct...

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Problems solved by technology

However, there are still problems in the current method. First, the time period division of the data set adopts the division method of uniform distribution of data volume, which does not consider the degree of drift change; second, the current classifier integration method is only used for qualitative analysis of gas types. It cannot be used for the identification of mixed gas concentration; the third is that in the support vector machine method adopted, the importance of each sample point in the pattern recognition process is not considered, and the prediction accuracy needs to be improved

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