CAJ | 학술논문

网络并发式流量特征具有信号时间可预测性，通过对网络流量的解卷积测度特征提取，提高对网络流量的预测性能。传统法方法采用粒子群优化算法实现对网络流量的特征测度盲解卷积分析，对原始信号的统计信息提取效果不好。提出一种基于粒子群退化重采样的网络流量解卷积测度提取算法，构建并发式网络流量序列采集模型，设计粒子退化重采样技术，将每个粒子的当前适应度值与其自身的个体最优值进行比较，如果优于个体最优值，得到粒子当前最优位置。仿真实验表明，采用该算法，收敛速度很快，在粒子群进化50代以内就可以实现成功收敛，对流量序列的测度特征提取结果准确，预测精度较高，展示了算法的优越性能。
망락병발식류량특정구유신호시간가예측성，통과대망락류량적해권적측도특정제취，제고대망락류량적예측성능。전통법방법채용입자군우화산법실현대망락류량적특정측도맹해권적분석，대원시신호적통계신식제취효과불호。제출일충기우입자군퇴화중채양적망락류량해권적측도제취산법，구건병발식망락류량서렬채집모형，설계입자퇴화중채양기술，장매개입자적당전괄응도치여기자신적개체최우치진행비교，여과우우개체최우치，득도입자당전최우위치。방진실험표명，채용해산법，수렴속도흔쾌，재입자군진화50대이내취가이실현성공수렴，대류량서렬적측도특정제취결과준학，예측정도교고，전시료산법적우월성능。
The network concurrent flow characteristics with signal time predictability, based on network flow solution of con?volution feature extraction, improve the performance of the prediction model of network traffic. The traditional method using particle swarm optimization algorithm to achieve the characteristic measurement of network traffic analysis of blind decon?volution, the statistical information of the original signal extraction effect is not good. Put forward a kind of particle swarm degradation of network traffic measure resampling deconvolution algorithm based on the construction of network traffic, and hair sequence acquisition model, design of particle degradation resampling technique, the current best individual fitness value of its own to compare the value of each particle, if better than individual optimal value, get the current best particle. Simulation results show that, by using this algorithm, the convergence speed is very fast, in the particle swarm evolution within 50 generations can achieve successful convergence, measure characteristics of flow sequence extraction result is ac?curate, the prediction accuracy is higher, it has the superior performance of the algorithm.