摘要
针对用户访问轨迹的数据特征,提出一种基于EEMD技术的多步时间序列预测模型。该模型利用了集合经验模态分解EEMD结合极限学习机ELM模型,混合人工鱼群MAFA优化的方式,克服了算法中存在过拟合和多步时间序列预测的策略限制问题。通过该模型,实现了对访问轨迹时间序列多步预测,结合安全范围包络线,进而提前发现是否存在入侵行为。验证结果表明,优化后的EEMD-ELM模型比传统时间序列预测方法的迭代速率与精度得到了极大提高,泛化能力增强,说明了该方法的有效性、可行性。
According to the data characteristics of the user access path, a multi-step time series prediction model based on ensemble empirical mode decomposition （EEMD） technology is proposed. The model uses the EEMD combining with the ex- treme learning machine （ELM） model, and optimization method of the hybrid artificial fish swarm algorithm to overcome the con- straint problems of the over-fitting and multi-step time series prediction strategy existing in the algorithm. The time series multi- step prediction of the access path was implemented with the model, and the intrusion behavior can be found in advance in com- bination with the envelope line of the safety range. The verification results show that the optimized EEMD-ELM model has higher iteration rate and accuracy than those of the traditional time series prediction methods, its generalization ability is enhanced, and the effectiveness and feasibility of this method was illustrated.
出处
《现代电子技术》
北大核心
2017年第7期159-162,166,共5页
Modern Electronics Technique
关键词
势态感知
集合经验模态
极限学习机
混合人工鱼群
多步时间序列预测
situation awareness
ensemble empirical mode
extreme learning machine
hybrid artificial fish swarm
multi-step time series prediction
