动态K-means与粒子群的网络态势预测优化算法A network situation prediction optimal algorithm based on dynamic K-means clustering and particle swarm optimization
魏雅娟;刘兆;刘意先;范九伦;
摘要(Abstract):
针对网络态势预测方法预测精度不高的问题,提出一种动态K-means与粒子群的网络态势预测优化算法。利用动态K-means聚类算法对收集到的网络基础指标进行分类,确定径向基函数(radial basis function,RBF)的网络中心和神经元宽度,使用粒子群最优化算法调整并确定RBF的权值系数,使权值分配更为合理,从而提高计算某局域网在某个时间段内所得态势预测值的精度。仿真结果表明,所提预测算法比基于广义的RBF网络态势预测算法的预测精度提高了14倍。
关键词(KeyWords): 动态K-means聚类;径向基函数;粒子群最优化算法;态势预测;预测精度
基金项目(Foundation): 国家自然科学基金项目(61671377,61601362)
作者(Author): 魏雅娟;刘兆;刘意先;范九伦;
Email:
DOI: 10.13682/j.issn.2095-6533.2020.05.006
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