文件名称:Application-of-optimized-Elman--
- 所属分类：
- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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- 305kb
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- 张*
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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. Experimental
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.
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. Experimental
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.
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优化Elman神经网络用于网络流量预测.pdf