Abstract: Elman neural network is a classical kind of recurrent neural network.Adaptive Quantum-Behaved Particle Swarm Optimization （AQPSO） algorithm is proposed in this paper 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 Zhongji stock data 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 stock data forecasting.

摘要： Elman神经网络是一种典型的递归神经网络。提出了自适应量子粒子群优化（Adaptive Quantum-Behaved Particle Swarm Optimization，AQPSO）算法，用于训练Elman网络参数，改进了Elman网络的泛化能力。利用中集集团股票数据进行预测，实验结果表明，采用AQPSO算法获得的Elman网络模型不但具有很强的泛化能力，而且具有良好的稳定性，在股票数据预测中具有一定的实用价值。