Sensor dynamic modelling based on NGO-LSSVM

Yongyan Shen; Liyue Fu

doi:10.1117/12.3049975

28 October 2024 Sensor dynamic modelling based on NGO-LSSVM

Yongyan Shen, Liyue Fu

Author Affiliations +

Proceedings Volume 13404, Fifth International Conference on Control, Robotics, and Intelligent System (CCRIS 2024); 1340405 (2024) https://doi.org/10.1117/12.3049975
Event: Fifth International Conference on Control, Robotics, and Intelligent System (2024), 2024, Macau, China

Abstract

Dynamic modeling of sensors is an important means to study their dynamic characteristics, laws of motion, and performance indicators. Aiming at the problem of high nonlinearity in sensor dynamic modeling, which makes it difficult to establish an accurate dynamic model, a sensor dynamic modeling method is proposed. The ideal parameters for the sensor dynamic model are chosen using the Northern Goshawk Optimization Algorithm, which is based on the regression prediction analysis method of the least squares support vector machine. Simulation shows that the NGO-LSSVM model is an economical, high-precision, and reliable sensor dynamic modeling tool.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongyan Shen and Liyue Fu "Sensor dynamic modelling based on NGO-LSSVM", Proc. SPIE 13404, Fifth International Conference on Control, Robotics, and Intelligent System (CCRIS 2024), 1340405 (28 October 2024); https://doi.org/10.1117/12.3049975

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE