Multispectral radiation thermometry inversion method based on improved bayesian optimization generalized regression neural network

By improving the Bayesian optimized generalized regression neural network, the problem of accurately solving the spectral emissivity in multispectral radiometric thermometry was solved, achieving efficient and accurate temperature inversion, which is suitable for non-contact measurement of high-temperature targets.

CN118298144BActive Publication Date: 2026-08-18XIDIAN UNIV
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Patent Information

Application Number
CN202410359706.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-03-27
Publication Date
2026-08-18
Estimated Expiration
2044-03-27

AI Technical Summary

Technical Problem

In existing multispectral radiation thermometry methods, the accurate solution of spectral emissivity has large uncertainties, which affects the temperature measurement accuracy and makes data processing complex, resulting in low temperature measurement efficiency.

Method used

An improved Bayesian optimization generalized regression neural network was adopted. By preprocessing infrared multispectral images, a generalized regression neural network model was constructed, and the hyperparameters were optimized using a biomimetic optimization algorithm. By combining Gaussian and exponential kernel functions, the true temperature of the target was retrieved.

Benefits of technology

It improves temperature measurement accuracy and efficiency, reduces reliance on emissivity model assumptions, and has wider applicability and higher temperature measurement performance.

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Abstract

The application discloses a multispectral radiation temperature measurement inversion method based on an improved Bayesian optimization generalized regression neural network, and comprises the following steps: carrying out pretreatment on an infrared multispectral image sequence, corresponding the pretreated data to a target real temperature, and constructing a data set; establishing a generalized regression neural network model for real temperature regression inversion of a target temperature measurement area; based on the data set, adopting a bionic optimization algorithm as a sampling method of Bayesian optimization, and optimizing hyperparameters in the generalized regression neural network model by using an improved Bayesian optimization algorithm, so as to inversely calculate the target real temperature by using the optimized generalized regression neural network model. The method does not need to assume an emissivity model, has higher temperature measurement efficiency and temperature measurement precision, and has wider applicability.
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