摘要
对量子粒子群优化(QPSO)算法进行研究,提出了自适应量子粒子群优化(Adaptive QPSO)算法,用于优化Elman神经网络的参数,改进了Elman神经网络的泛化能力。利用网络流量时间序列数据进行预测,实验结果表明,采用AQPSO算法优化获得的Elman神经网络模型不但具有较强的泛化能力,而且具有良好的稳定性,在网络流量时间序列数据的预测中具有一定的实用价值。
Quantum-behaved particle swarm optimization （QPSO） algorithm is researched and adaptive quantum-behaved particle swarm optimization （AQPSO） algorithm is proposed in order to improve network＇ s performance. By applying AQPSO algorithm to train the net parameters adopted in the Elman neural network, the generalization ability of the Elman neural network is improved. Ex- perimental results with network traffic time series data forecasting sets show that obtained network model has not only good generalization properties, but also has better stability. It illustrates that Elman net with AQPSO optimization algorithm has the promising application in network traffic time series data prediction.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第17期4531-4534,共4页
Computer Engineering and Design
基金
国家自然科学基金项目(60675011)
关键词
艾尔曼
量子粒子群优化
神经网络
网络流量
预测
Elman
QPSO （quantum-behaved particle swarm optimization）
neural network
network traffic
forecasting
