Sensor measuring method based on double support vector machines

Abstract

The invention discloses a sensor measuring method based on double support vector machines, belonging to the field of sensor information processing. The method comprises the following steps: establishing a data sample by sensor calibration data under the working condition environment; constructing a sensor inverse model based on the support vector machines by applying the data sample; establishing a second data sample by applying error data of sensor output and sensor inverse model output to construct an error model based on the support vector machines; selecting and optimizing the parameters of the sensor inverse model and the error model through a quantum-behaved particle swarm optimization algorithm, as well as minimum standards of root-mean-square error of the model and maximum absolute error; and taking the difference of the sensor inverse model output and the error model output as a measured true valve. The sensor measuring method has the advantages that the influence of non-linearity property of the sensor on the measuring result can be effectively lowered; the measured high-precision measurement is realized; the modeling working amount is low; and the sensor measuring method based on the double support vector machines can be widely applied to the field of high-precision measurement of various sensors.

Description

technical field [0001] The invention belongs to the field of sensor information processing, in particular to a sensor measurement method based on dual support vector machines. Background technique [0002] Sensors have been widely used in many fields such as industry, agriculture, national defense, science and technology, and have become the basis of modern information society. Due to the influence of many factors such as the characteristics of sensor sensitive elements, application environment, sensor aging, etc., there is a complex nonlinear relationship between the output and input of the sensor, which brings large measurement errors in practical engineering applications. [0003] At present, the sensor inverse modeling method based on machine learning is an effective technical means to realize the compensation of the nonlinear characteristics of the sensor and improve the measurement accuracy of the sensor. Neural network has strong nonlinear mapping ability and strong ...

AI Technical Summary

Problems solved by technology

[0005] Due to the high nonlinearity of the input and output characteristics of the sensor, the inverse modeling based on the support vector machine cannot fit the inverse characteristics of the sensor with high precision, and there are still considerable measurement errors in this method

Images