<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To improve the recognition of female images in Qing Dynasty literature, this paper designs a multivariate nonlinear-based image dynamic programming model. Data-driven typical correlation analysis is used to extract the key controllable variables with the strongest correlation with female image features and use them as input variables for modeling so as to make the maximum correlation between different image features and improve the modeling efficiency. The nonlinear spatial state model is established by using the subspace identification method, and the female image planning model is substituted into the multivariate nonlinear analysis, and the parameters of the nonlinear fitting function about the image planning can be obtained. The simulation analysis of the image dynamic planning model based on multivariate nonlinearity is carried out, and the results show that the average annual failure time of the model designed in this paper is 12.6 hours, and the classification accuracy in female image classification is 96.3%, and the classification time is stable at 1.162 s. The results show that the multivariate nonlinear features of the model can carry out dynamic planning of female images in Qing dynasty literature and improve the classification of image feature recognition.</p>
</abstract>

To improve the recognition of female images in Qing Dynasty literature, this paper designs a multivariate nonlinear-based image dynamic programming model. Data-driven typical correlation analysis is used to extract the key controllable variables with the strongest correlation with female image features and use them as input variables for modeling so as to make the maximum correlation between different image features and improve the modeling efficiency. The nonlinear spatial state model is established by using the subspace identification method, and the female image planning model is substituted into the multivariate nonlinear analysis, and the parameters of the nonlinear fitting function about the image planning can be obtained. The simulation analysis of the image dynamic planning model based on multivariate nonlinearity is carried out, and the results show that the average annual failure time of the model designed in this paper is 12.6 hours, and the classification accuracy in female image classification is 96.3%, and the classification time is stable at 1.162 s. The results show that the multivariate nonlinear features of the model can carry out dynamic planning of female images in Qing dynasty literature and improve the classification of image feature recognition.

A multivariate nonlinear dynamic programming of female figures in Qing dynasty literature

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To improve the recognition of female images in Qing Dynasty literature, this paper designs a multivariate nonlinear-based image dynamic programming model....

A multivariate nonlinear dynamic programming of female figures in Qing dynasty literature

Published Online: Oct 07, 2023

Page range: -

Received: Nov 24, 2022

Accepted: Apr 17, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00514

Keywords
Nonlinear model, Female image planning, Spatial identification method, Typical correlation analysis, Classification accuracy

© 2023 Jing Zeng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A multivariate nonlinear dynamic programming of female figures in Qing dynasty literature

Published Online: Oct 07, 2023

Page range: -

Received: Nov 24, 2022

Accepted: Apr 17, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00514

KeywordsNonlinear model, Female image planning, Spatial identification method, Typical correlation analysis, Classification accuracy

© 2023 Jing Zeng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Nonlinear model, Female image planning, Spatial identification method, Typical correlation analysis, Classification accuracy