Multifunctional senor sample selection method based on kernel subtractive clustering

Abstract

The invention relates to sensors, and discloses a multifunctional senor sample selection method based on kernel subtractive clustering. The multifunctional senor sample selection method includes the following steps: step 1, sampling is carried out on a sensor which needs to be subjected to signal reconstruction, and sample data are obtained; step 2, the sample data obtained in the step 1 is subjected to normalization processing; step 3, the kernel subtractive clustering is used for mapping the sample data, which are already subjected to the normalization processing, to a high-dimensional space to enable the sample data to be linearly separable, and the sample data are classified; and step 4, a clustering center of each type of the sample data is used as a sample point to be selected. According to the multifunctional senor sample selection method, on the basis that the reconstruction accuracy is guaranteed, the number of the sample data which are needed for the signal reconstruction is reduced. The fact that each senor is subjected to high-density sampling is not needed, and therefore workloads which are needed for the sampling are greatly reduced.

Description

technical field [0001] The invention relates to sensors, in particular to a multifunctional sensor sample selection method based on kernel subtraction clustering. Background technique [0002] Multifunctional sensor is a sensor that can measure multiple physical quantities at the same time, which is one of the main development trends of modern sensing technology. With the development of multifunctional sensors, the corresponding signal reconstruction methods must be studied. The factors affecting the signal reconstruction accuracy of multifunctional sensors include the selection and distribution of training sample data in addition to the signal reconstruction method itself. A set of training samples with reasonable distribution can not only ensure the reconstruction accuracy well, but also effectively reduce the number of sample data, thereby reducing the workload required for sampling, which is of great significance to the signal reconstruction of large-scale multifunction...

AI Technical Summary

Problems solved by technology

However, for a certain sensor, the distribution of non-linearity in each area of ​​its input is not uniform, if the sampling is carried out based on the area with low non-linearity, it is difficult to meet the accuracy requirements; in order to meet the accuracy requirements, It is necessary to use a smaller sampling interval in the entire domain of definition, which causes waste in areas with low nonlinearity

This is acceptable for the signal reconstruction of a certain sensor, but for the signal reconstruction of a large number of sensors, a lot of manpower and material resources will be wasted

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