基于改进鲸鱼优化贝叶斯网络参数学习算法的无人机态势评估

IF 4.4 2区 地球科学 Q1 REMOTE SENSING Drones Pub Date : 2023-11-01 DOI:10.3390/drones7110655
Weinan Li, Weiguo Zhang, Baoning Liu, Yicong Guo
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引用次数: 0

摘要

为实现无人机态势评估,建立了一种态势评估贝叶斯网络。针对神经网络参数难以获取的问题,提出了一种基于先验参数区间的改进鲸鱼优化算法(IWOA-PPI)进行参数学习。首先,根据情境与其相关因素之间的依赖关系,建立情境网络的结构。其次,为了充分挖掘参数的先验知识,利用蒙特卡罗采样和区间变换公式将参数约束转化为参数先验区间;第三,提出了一种变量环因子和一种非线性收敛因子。前者和后者分别增强了鲸鱼优化算法(WOA)的局部和全局搜索能力。最后,提出了一种结合Levy飞行的模拟退火策略,使WOA能够跳出局部最优。在标准神经网络的实验中,应用了5种参数学习算法,结果证明了IWOA-PPI不仅有效而且最准确。在态势BN的实验中,对假设任务场景的态势进行了评估,结果表明本文提出的态势评估方法是正确可行的。
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The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm
To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible.
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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