CAJ | 학술논문

在网络预测算法中传统的预测几乎都没有考虑流量的自相似性和高斯性，仅仅利用最大Lya-punov指数进行计算机网络流量的混沌性检验，对网络流量的预测也仅仅是以计算得到的最大Lya-punov指数为前提，算法精度受限。提出一种改进的Wolf一步预测算法，对网络流量通过自相似的FGN (FGN, fractional gaussian noise)过程处理，得到替代原网络流量的新的序列，新的替代流量序列具有自相似性，从而进行预测。仿真结果准确检验了网络流量的混沌性，预测结果表明，改进的预测算法在略有缩短最大预报时间下，精度却高很多，预测的误差小于3%的点比例比原传统算法提高了20%以上。
재망락예측산법중전통적예측궤호도몰유고필류량적자상사성화고사성，부부이용최대Lya-punov지수진행계산궤망락류량적혼돈성검험，대망락류량적예측야부부시이계산득도적최대Lya-punov지수위전제，산법정도수한。제출일충개진적Wolf일보예측산법，대망락류량통과자상사적FGN (FGN, fractional gaussian noise)과정처리，득도체대원망락류량적신적서렬，신적체대류량서렬구유자상사성，종이진행예측。방진결과준학검험료망락류량적혼돈성，예측결과표명，개진적예측산법재략유축단최대예보시간하，정도각고흔다，예측적오차소우3%적점비례비원전통산법제고료20%이상。
According to the nonlinearity and chaotic property feature of the network traffic flow, the prediction of the net-work traffic flow was going to be done precisely. An improved one step Wolf prediction algorithm was proposed with the (FGN) Fractional Gaussian Noise processing. The surrogate data of the original traffic flow data was gotten, and the new surrogate data series had the property of self-similarity for the prediction. The simulation result test the chaotic property precisely, and the prediction result show that the improved prediction algorithm has much high precision with the price of shortening the maximum prediction time a little. The position ratio of prediction error which less than 3% is increased 20%more than traditional algorithm.