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Mechanical temperature instrument error prediction method based on genetic-algorithm optimized least square support vector machine

A technology of support vector machine and least squares, applied in thermometer testing/calibration, thermometers, instruments, etc., can solve problems such as long training time, shortened training time, and low accuracy

Active Publication Date: 2016-03-30
邳州市润宏实业有限公司
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Problems solved by technology

[0004] In order to overcome the deficiencies of low precision, complex calculation and long training time of the error compensation method of the existing mechanical temperature instrument, the present invention provides a genetic algorithm-based optimized least squares support with low precision, simplified calculation and shortened training time Error Prediction Method of Mechanical Temperature Instrument Based on Vector Machine

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  • Mechanical temperature instrument error prediction method based on genetic-algorithm optimized least square support vector machine

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[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] refer to Figure 1 to Figure 9 , a mechanical temperature instrument error prediction method based on genetic algorithm optimization least squares support vector machine, the prediction method comprises the following steps:

[0046] (1) Obtain model input and output, use the characteristic parameters of the measured mechanical temperature instrument as model input, and sample the error value and error change rate of the instrument as model output;

[0047] (2) Preprocess the original temperature error data, normalize the data to the [-1, 1] interval, generate data sets and group them to obtain training sets and test sets;

[0048] (3) Select the Gaussian radial basis kernel function as the kernel function of the least squares support vector machine model, and determine the parameter combination of the model (σ 2 , γ), where γ is the kernel parameter, σ 2 is t...

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Abstract

A mechanical temperature instrument error prediction method based on a genetic-algorithm optimized least square support vector machine is disclosed. The method comprises the following steps of (1) taking a tested characteristic parameter of a mechanical temperature instrument as model input, and taking an instrument error value and an error change rate acquired through sampling as model output; (2) carrying out pretreatment on original temperature error data; (3) selecting a Gauss radial kernel function as a kernel function of a least square support vector machine model; (4) using a genetic algorithm to optimize a parameter combination of the least square support vector machine; (5) constructing a mechanical temperature instrument error prediction model based on the genetic-algorithm optimized least square support vector machine; (6) inputting a data set and using a model obtained through training to carry out prediction; (7) comparing a temperature instrument error prediction result with an actual temperature error and analyzing a temperature error value and a change trend of a temperature error change rate. By using the method, precision is high; calculating is simple and engineering practicality is high.

Description

technical field [0001] The invention designs a method for predicting the error of a mechanical temperature instrument, in particular a method for predicting the error of a mechanical temperature instrument based on a genetic algorithm optimization least square support vector machine. Background technique [0002] In the field of automated process instrumentation, temperature, as one of the most basic detection parameters, is widely used in the fields of petrochemical industry, safety production and automobile industry. With the continuous increase of practical application occasions, the environment for measuring temperature is becoming more and more harsh, making the mechanical temperature instrument with good stability and strong anti-interference ability become the main temperature measuring instrument in the occasion with many interference signals. The pressure temperature instrument is more common in practical applications. The closed system is filled with low boiling po...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01K15/00
CPCG01K15/007
Inventor 叶永伟陆俊杰王永兴钱志勤杨超
Owner 邳州市润宏实业有限公司
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