Electronic nose data mining method based on supervised explicit manifold learning algorithm

Abstract

The invention relates to a method for mining data of an electronic nose based on supervised explicit manifold learning algorithm. The method for mining the data of the electronic nose through the explicit manifold learning algorithm comprises the following steps of collection of gas samples, characteristic extraction of the gas samples, determination of near neighbor of each point in a characteristic value matrix, relation calculation of any two characteristic value points and data dimension reduction of the explicit manifold algorithm. The data mining method of the electronic nose with the supervised explicit manifold learning algorithm comprises all above steps and is additionally provided with one step after the characteristic extraction of the gas sample: considering the type information, and determining the near neighbor of each point in the characteristic value matrix. The method has beneficial effects that the explicit manifold learning algorithm is used for reducing the dimension of the electronic nose data, and an explicit dimensional-reduction expression is provided; and the supervised manifold learning algorithm is used for reducing the dimension of the electronic nose data, the relation difference of each point of difference sources in the characteristic value matrix is considered, and the reservation of the detail information guarantees high resolution of an electronic nose system.

Description

technical field [0001] The invention relates to the field of electronic nose gas detection, in particular to an electronic nose data mining method based on a supervised explicit manifold learning algorithm. Background technique [0002] The gas sensor array of a modern electronic nose system usually contains dozens of odor sensors, and the optical sensor array even contains hundreds or even thousands of sensing units. The dimensionality of the gas sample data obtained from this array is quite large. The effect of data input to artificial intelligence algorithm for pattern discrimination is very unsatisfactory, mainly because the sensor array of the electronic nose has the characteristics of cross-sensitivity, that is, multiple units in the sensor array will respond to the same gas, so While reducing the risk of affecting system decision-making due to abnormal work of individual sensors, it also increases the redundancy of data. [0003] The electronic nose data mining proce...

AI Technical Summary

Problems solved by technology

[0006] 1. Because the manifold learning algorithm focuses on maintaining the local structure, it cannot give an explicit mapping expression, which leads to it can only reduce the dimensionality of the training data of the electronic nose system, but cannot reduce the dimensionality of the newly collected data. As a result, the electronic nose system using the manifold learning algorithm cannot be applied to the practice of gas detection;

[0007] 2. The traditional manifold learning algorithm is an unsupervised algorithm. When maintaining the local structure of the training data of the electronic nose system, it does not consider the difference between the relationship between the feature value points within the class and between the classes. This detail information is ignored. It directly leads to an error in the pattern discrimination of the electronic nose

[0008] From the current domestic literature research, there is no report on the use of supervised explicit manifold learning algorithm for electronic nose data mining

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