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Received:November 09, 2012 Revised:December 23, 2012
Received:November 09, 2012 Revised:December 23, 2012
中文摘要: 针对BP神经网络易陷入局部极小问题以及收敛速度慢的问题, 引入量子粒子群优化算法和BP神经网络相结合的方法, 共享BP神经网络强大的灵活性和量子粒子群全局搜索能力强的优势, 通过改进QPSO的平均最优位置的计算方法, 实现基于BP神经网络和量子粒子群的油田节能指标预测. 以大庆某采油厂注水泵机组单耗数据为训练数据, 预测结果表明该方法能达到良好的预测效果, 具有可行性.
Abstract:According to the fact that BP neural network is easy to fall into local minimum and the slow convergence problems, the paper introduces QPSO and BP neural network combination method, which shares the advantage of BP neural network robust flexibility and the powerful global searching ability of QPSO, through improved the calculation method of average optimal position of QPSO to make the BP neural network and QPSO oilfield energy conservation index prediction success. Using the injection pump unit consumption data of Daqing Oilfield Company as training data, by training the new mehtod with the data of samples, the forecast results show that the proposed method can achieve good forecast effect and have feasibility.
keywords: bp neural network quantum partical swarm index prediction algorithm optimization moving average
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基金项目:国家自然科学基金(61170132);国家重大专项(2011ZX05020-007);黑龙江省教育厅科学技术研究项目(12521055)
引用文本：
尚福华,杨慧,张吉峰,马明梅,董桂苓.基于QPSO的BP神经网络油田节能指标预测.计算机系统应用,2013,22(6):95-97,185
SHANG Fu-Hua,YANG Hui,ZHANG Ji-Feng,MA Ming-Mei,DONG Gui-Ling.Oilfield Energy Saving Index Prediction Based on QPSO and BP Neural Network.COMPUTER SYSTEMS APPLICATIONS,2013,22(6):95-97,185
尚福华,杨慧,张吉峰,马明梅,董桂苓.基于QPSO的BP神经网络油田节能指标预测.计算机系统应用,2013,22(6):95-97,185
SHANG Fu-Hua,YANG Hui,ZHANG Ji-Feng,MA Ming-Mei,DONG Gui-Ling.Oilfield Energy Saving Index Prediction Based on QPSO and BP Neural Network.COMPUTER SYSTEMS APPLICATIONS,2013,22(6):95-97,185
