摘要
针对多机协同对抗过程中的编批问题,设计了一种基于改进自组织迭代聚类的多机协同编批方法.该方法解决了传统自组织迭代聚类算法中人工参数设置不便利不直观的问题,能够在给定少数直观超参数条件下,使多机自主调整聚类过程中所涉及的参数,最终迭代出合理的编批结果.首先对高维多机态势信息进行标准化和主成分分析处理,从而确认新的向量空间;然后引入密度聚类中的邻域密度判别思想对传统自组织迭代聚类方法的合并和分裂操作进行改进,优化并减少了传统方法进行分裂和合并操作所涉及的人工参数,提升了执行编批聚类任务的智能自主性;最后选取算法评价指标,使用所提算法以及传统算法对多个人工合成数据以及实际想定场景进行聚类测试并对测试结果进行评价.人工合成数据仿真表明改进自组织迭代聚类算法在优化聚类过程中的人工参数后仍与原始算法表现出相当的性能,实际想定场景的编批结果进一步说明了改进自组织迭代聚类算法在具体应用场景中的有效性以及在未来实际场景中的实用性.
Abstract
This article addresses the bathing problem in multi-machine collaborative operations,proposing a method based on improved self-organizing iterative clustering. This approach circumvents the issues of traditional manual parameter setting in the self-organizing iterative clustering algorithm that is often inconvenient and non-intuitive. The proposed method allows multiple machines to autonomously adjust the parameters involved in the clustering process,given a small number of intuitive hyperparameters. The ultimate goal is to iterate toward reasonable editing results. Initially,this article focuses on selecting feature vectors for the multi-machine collaborative confrontation situation. It applies standardization and principal component analysis to high-dimensional multi-machine situation information to confirm the new vector space. This space mainly encompasses position information in three dimensions and speed information. Subsequently,the paper introduces the concept of neighborhood density discrimination from density clustering. This improves the merging and splitting operations of the traditional self-organizing iterative clustering method. It optimizes and reduces the artificial parameters involved in these operations,enhancing the intelligent autonomy for batch clustering tasks. Before optimization,artificial parameters primarily include the number of expected clusters,minimum number of points within a class,number of iterations,upper limit of standard deviation that limits data distribution within a class,and an allowable shortest distance indicator between classes. Post optimization,the artificial parameters are limited to the expected cluster quantity,minimum number of points,and the number of iterations within a single classification. These optimized parameters are relatively intuitive,and the algorithm output does not strongly correlate with the input parameters. Ultimately,the paper selects algorithm evaluation indicators,including Dunn,Davies-Bouldin,silhouette coefficient,and Calinski-Harabasz. It uses these to evaluate the proposed algorithms ISODATA+and K-MEANS+,along with the original ISODATA algorithm,against multiple artificially synthesized data sets (completely random data,Gaussian-generated data,and sin-type data) and real-world scenarios. The experimental results suggest that while KMEANS+shows significant advantages owing to multiple manually set hyperparameters,it requires constant debugging when adjusting parameters,which increases the complexity of the task. Compared with the original self-organizing iterative algorithm ISODATA,statistical results show that the improved algorithm has equivalent capabilities to the original algorithm. This demonstrates that the ISODATA+algorithm maintains good clustering capabilities even after removing some artificial parameters. The batching results from actual scenario tests further illustrate the effectiveness of the improved self-organizing iterative clustering algorithm in specific application scenarios,demonstrating its practicability for future real-world applications.
维普
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