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.

优化Elman神经网络用于网络流量预测.pdf

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