计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
9期
106-109
,共4页
短时交通流量%相空间重构%布谷鸟搜索算法%高斯扰动%反向传播(BP)神经网络
短時交通流量%相空間重構%佈穀鳥搜索算法%高斯擾動%反嚮傳播(BP)神經網絡
단시교통류량%상공간중구%포곡조수색산법%고사우동%반향전파(BP)신경망락
short time traffic flow%phase space reconstruction%cuckoo search algorithm%Gaussian disturbance%Back Propaga-tion(BP)neural network
为了提高短时交通流量的预测精度,提出一种布谷鸟搜索算法优化 BP 神经网络参数的短时交通流量预测模型(CS-BPNN).基于混沌理论对短时交通流量时间序列进行相空间重构,将重构后的时间序列输入到 BP神经网络进行学习,采用布谷鸟搜索算法找到 BP 神经网络最优参数,建立短时交通流量预测模型,通过具体实例对 CS-BPNN 性能进行测试.仿真结果表明,相对于对比模型,CS-BPNN 提高了短时交通流量的预测精度,更加准确反映了短时交通流量的变化趋势.
為瞭提高短時交通流量的預測精度,提齣一種佈穀鳥搜索算法優化 BP 神經網絡參數的短時交通流量預測模型(CS-BPNN).基于混沌理論對短時交通流量時間序列進行相空間重構,將重構後的時間序列輸入到 BP神經網絡進行學習,採用佈穀鳥搜索算法找到 BP 神經網絡最優參數,建立短時交通流量預測模型,通過具體實例對 CS-BPNN 性能進行測試.倣真結果錶明,相對于對比模型,CS-BPNN 提高瞭短時交通流量的預測精度,更加準確反映瞭短時交通流量的變化趨勢.
위료제고단시교통류량적예측정도,제출일충포곡조수색산법우화 BP 신경망락삼수적단시교통류량예측모형(CS-BPNN).기우혼돈이론대단시교통류량시간서렬진행상공간중구,장중구후적시간서렬수입도 BP신경망락진행학습,채용포곡조수색산법조도 BP 신경망락최우삼수,건립단시교통류량예측모형,통과구체실례대 CS-BPNN 성능진행측시.방진결과표명,상대우대비모형,CS-BPNN 제고료단시교통류량적예측정도,경가준학반영료단시교통류량적변화추세.
In order to improve the prediction accuracy of short time traffic flow,this paper proposes a network traffic prediction model based on Cuckoo Search algorithm and BP Neural Network(CS-BPNN).The time series of short time traffic flow is recon-structed to form a multidimensional time series based on chaotic theory,and then the time series are input into BP neural net-work to learn which parameters of BP neural network are optimized by cuckoo search algorithm to find the optimal parameters and establish the short time traffic flow prediction model.The performance of CS-BPNN is tested by the simulation experiments. The simulation results show that the proposed model improves the prediction accuracy of short time traffic flow and can more describe network traffic complex trend compared with reference models.
