期刊
MATHEMATICS
卷 10, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/math10101749
关键词
swarm intelligence; parameter estimation; controlled autoregressive; aquila optimizer
类别
This study investigates the parameter estimation of the CAR model using a novel AO algorithm, demonstrating the accuracy, convergence, and robustness of the AO under various noise levels.
Swarm intelligence-based metaheuristic algorithms have attracted the attention of the research community and have been exploited for effectively solving different optimization problems of engineering, science, and technology. This paper considers the parameter estimation of the control autoregressive (CAR) model by applying a novel swarm intelligence-based optimization algorithm called the Aquila optimizer (AO). The parameter tuning of AO is performed statistically on different generations and population sizes. The performance of the AO is investigated statistically in various noise levels for the parameters with the best tuning. The robustness and reliability of the AO are carefully examined under various scenarios for CAR identification. The experimental results indicate that the AO is accurate, convergent, and robust for parameter estimation of CAR systems. The comparison of the AO heuristics with recent state of the art counterparts through nonparametric statistical tests established the efficacy of the proposed scheme for CAR estimation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据