Performance Prediction of Radar Seeker Based on Improved PSO-SVR Model
CHEN Xiyi
HUANG Zhaonian
GUO Chen
WANG Ying
Abstract：Aiming at the problems of insufficient test data of radar seeker performance and low prediction accuracy of tradition-al SVR algorithm,this paper introduces an improved PSO-SVR algorithm to build a seeker performance state prediction model.Firstly,PSO algorithm is used to optimize kernel parameters and penalty factors in SVR model to achieve automatic optimization of the optimal hyperparameter set.Secondly,in order to solve the problem that PSO algorithm is easy to fall into local optimal,dynam-ic inertia weight is introduced into PSO algorithm to improve its solving accuracy.Finally,the actual data is used for simulation anal-ysis.The results show that this method has better accuracy and robustness than the traditional SVR model and has a good application prospect.
Keywords：support vector regressionparticle swarm optimizationinertia weightprediction of performance
Publication Date：2023-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 96-100 )