摘要
为了解决IP业务流量的建模与预测自动化问题,该文针对传统GP模型在流量行为分析对预测精度和外推性能方面存在的不足,提出了在原有模型基础上引入常微分方程进行GP(遗传规划)演化,从而提高了模型的动态描述能力,改善了模型的复杂度,更能贴切反映业务流量的变化规律。
In order to solve the problem of IP network traffic modeling and predicting, this thesis puts forward an ameliorated model, which is included ordinary differential equation based on the traditional GP model that has some shortages on the precision of traffic behavior and the extrapolated capability of the evolvement. The dynamic description capability of the model can be improved. The complexity of the model can be reformed, and the IP network traffic transformation orderliness can be reflected accurately.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2006年第23期208-210,213,共4页
Computer Engineering
基金
国家自然科学基金资助项目(60573088)
河北省自然科学基金资助项目(603407)
关键词
IP业务流量预测
遗传规划
常微分方程
适应度
IP traffic prediction
Genetic programming （GP）
Ordinary differential equation
Fitness
