[1]邓宗强,曾碧卿.一种新参数优化算法及其在流量预测中的应用[J].计算机技术与发展,2013,(10):36-40.
　DENG Zong-qiang[],ZENG Bi-qing[].A New Parameter Optimization Algorithm and Its Application in Traffic Prediction[J].,2013,(10):36-40.
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一种新参数优化算法及其在流量预测中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年10期
页码:
36-40
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
A New Parameter Optimization Algorithm and Its Application in Traffic Prediction
文章编号:
1673-629X（2013）10-0036-05
作者:
邓宗强1; 曾碧卿2
[1]华南师范大学 计算机学院;[2]华南师范大学南海校区 信息工程与技术系
Author(s):
DENG Zong-qiang[1]; ZENG Bi-qing[2]
关键词:
量子粒子群算法; 参数优化; 小波变换; 最小二乘支持向量机; 流量预测
Keywords:
quantum-behaved particle swarm optimization; parameters optimization; wavelet transformation; least squares support vector machines; traffic prediction
文献标志码:
A
摘要:
为了提高网络流量预测的精度，提出先进行小波变换后利用LS-SVM的网络流量预测模型，对于LS-SVM参数的优化，提出一种基于模拟退火算法的自适应混沌量子粒子群算法( AS-QPSO)。该算法在量子粒子群算法的基础上加入了自适应和混沌特性，使算法具有动态自适应性，改善算法的全局寻优能力，再引入模拟退火算法避免陷入局部最优，使算法具有更好的收敛性和稳定性。实验结果表明：与其他算法优化的LS-SVM模型相比，该模型具有较好的泛化能力﹑更高的预测精度以及很好的稳定性
Abstract:
For improving the prediction accuracy of network traffic,a new network traffic prediction model is proposed based on wavelet transform and optimized LS-SVM. To optimize the parameters of LS-SVM,a kind of adaptive chaos quantum-behaved particle swarm optimization based on simulated annealing algorithm ( AS-QPSO) is proposed. The algorithm joins adaptive and chaotic characteristics based on QPSO,making it dynamic adaption,improving the capacity of the global optimization. Then the simulated annealing algorithm is introduced to avoid falling into local optimum,the algorithm has better convergence and stability. Experimental results show that com-pared with other algorithm optimized LS-SVM model,the proposed model is more efficient with higher precision,better generalization performance and stability

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更新日期/Last Update: 1900-01-01