摘要
针对带混沌特性的网络流量在线预测,提出一种融合自适应粒子群算法(APSO)和递推式最小二乘支持向量机回归的流量模型。对流量序列嵌入重构得到多维状态输入矢量,将其作为初始LSSVM的训练样本,其中采用自适应粒子群算法对模型的特征参数、嵌入维数寻优,避免早熟停滞。对于在线预报过程中的吸收样本、删减样本采用核矩阵迭代式求解,动态调整回归机,使得模型具有在线学习能力,由此得APSO-LSSVM在线流量预测模型,并考察网络负荷度与嵌入维数关系。仿真实验表明:该方法能有效预测网络流量,实现较高精度实时流量估计。
For online prediction of network traffic with chaos characteristics, a new network traffic prediction model based on adaptive par- ticle swarm optimisation algorithm （APSO） and recursive least square support vector machine （LSSVM） is proposed. First, the network traf- fic sequence is conducted the embedment and reconstruction to acquire multidimensional states input vectors, and they are used as the initial training sample of LSSVM. The adaptive particle swarm optimisation is used to optimise the characteristic parameters and the embedded di- mensions of the model for avoiding premature stagnation. At last, the iterative expression of kernel matrix is adopted in solution of absorption and deduction of the samples in online prediction process, thus to dynamically adjust the regression machine and have the model possesses on- line learning ability. In this way, the online APS0-LSSVM network traffic prediction model is formed, and then it need to explore the relation- ship between the network lbad and the dimensions embedded as well. Simulation experiment shows that the APSO-LSSVM model is able to predict network traffic effectively and get higher accuracy in real time traffic estimation.
出处
《计算机应用与软件》
CSCD
北大核心
2013年第9期21-24,127,共5页
Computer Applications and Software
基金
国家自然科学基金项目(61164011)
关键词
网络流量预测
最小二乘支持向量机
自适应粒子群
参数优化
迭代求解
Network traffic prediction LSSVM Adaptive particle swarm optimisation Parameter optimisation Iterative solution
