采油螺杆泵转子转速优化方法研究
|
摘要
螺杆泵采油是一种新型的人工举升方式，具有结构简单、高效节能、机组占地面积小等特点，尤其适宜于高粘度、高含砂量、高油气（水）比原油开采，已经在国内外的油田生产中普遍使用。在螺杆泵采油过程中，转速的选择十分重要，直接影响到泵的效率和使用寿命。如果转速选择不合理，将引发油井抽空“烧泵”、泵效下降以及使用寿命缩短等诸多问题。目前，国内外已经有研究人员采用建立最佳转速模型的方法来选择合理的转速，其不足之处在于没有考虑泵的结构参数和油井工况的耦合影响，而且所建立的模型收敛速度慢、精度低；没有对转速的智能控制问题进行深入研究，对提高泵效、延长寿命的效果仍不明显。为此，本文从理论和实验两方面开展了采油螺杆泵转子转速优化方法的研究，旨在进一步提高螺杆泵采油作业中转速控制技术水平。
论文首先分析了螺杆泵结构和工作原理，综述了螺杆泵采油技术的国内外发展状况，重点对螺杆泵采油技术领域中存在的关键技术问题进行了剖析，确定了论文工作的切入点。
速度是影响螺杆泵定子橡胶寿命的主导因素，研究他们之间的关系正是论文工作的核心。但是速度对橡胶磨损的影响又受制于螺杆泵结构参数和油井工况参数，这种影响呈非线性耦合形态。对此，文中在梳理了两大类影响因素的基础上，对解决非线性耦合关系问题的方法进行了考证，提出基于人工神经网络技术建立螺杆泵转速优化模型的思路，选取原油温度、原油粘度、螺杆泵泵端压差和螺杆泵定子橡胶磨损量作为输入量，以螺杆泵转速作为输出量，模拟实际工况并以其为参考考察优化效果。文中阐述了基于四种典型的人工神经网络模型（BP网络、RBF网络、Elman网络和GA-BP网络）进行优化的具体算法及优化结果与实际值的对比分析过程，从中遴选了比较理想的优化模型。
在螺杆泵采油系统的实际工作过程中，转速需根据实际工况的变化而变化。为实现螺杆泵转速的实时控制，自行开发了螺杆泵转速优化系统。文中介绍了软硬件开发环境、软件中内含的算法以及基于此平台对螺杆泵转速及其影响因素的测量和实时调控等操作过程。
为验证螺杆泵转速优化模型的有效性，并对转速优化问题深入研究，自行设计并研制了能够模拟实际油井工况的螺杆泵转速优化结果检验平台。该实验平台以环-块式摩擦磨损试验机为主体，结合所开发的螺杆泵转速优化系统，能够完成螺杆泵转速优化模型实效性的验证以及不同控制方式实效性的试验研究。
在螺杆泵转速优化效果检验平台上，对基于人工神经网络优化模型获得的螺杆泵转速优化结果进行了实验分析。采用不同试验方案（橡胶配方）进行了试验，对比分析实验结果可以看出，基于BP神经网络优化模型优化出的转速对减轻螺杆泵定子橡胶磨损量的效果比较明显。
最后，对进一步完善基于人工神经网络的螺杆泵转速优化平台提出了建设性思路，给出了优化系统的开放性架构和与实际信号采集子系统的接口。
As a new artificial lift method, progressing cavity pump (PCP) oil production hasbeen widely used in the oil fields at home and abroad for its characteristics includingsimple structure, high efficiency energy saving, small floor area and so on. PCP isespecially suitable for the oil well of production high viscosity oil, high sand content andhigh gas content. The choice of rotor speed of PCP speed is very important in the oilproduction; the rotor speed will directly affect the pump efficiency and service life. If theselection of rotor speed is unreasonable, it will cause “burn pump”, lower pump efficiency,shorten service life and other problems. Recently, researchers at home and abroad haveused the method of establishing the best speed model to choose reasonable speed. Thedisadvantage of the best speed model is that it does not consider the coupling influence ofgeometric parameters and structure parameters, and the convergence speed of establishedmodel is slower and accuracy is lower. Meanwhile, the researchers have not further studyin the problem of speed intelligent control, and the effect is still not obvious to improvepump efficiency and prolong life. Therefore, this paper studied PCP speed optimizationmethod from the theory and experiment both aspects, and trying to improve the level ofPCP speed control technology.
In the first place, the PCP structure and its working principle were analyzed in thearticle. Then, development of the PCP technology both at home and abroad wassummarized. The key problems of oil production technology of PCP were deeply discussedand the breakthrough point of this work was determined.
Speed is the dominant factor of affecting the service life of PCP stator rubber;therefore the core of this paper was study the relationship between them. However, theinfluence of speed on rubber wear depends on structure parameters and working conditionparameters and the influence shows a nonlinear coupling relationship. Therefore, on thebasis of combing the two groups of influence factors, the paper made a textual research forthe method of solving nonlinear coupling. The thought of establishing optimization model of PCP speed on the basis of artificial neural network was proposed. In order to simulatethe actual working condition and investigate optimization effect, the oil temperature, oilviscosity, pressure difference of pump ends and wear loss of stator rubber were selected asinput, and PCP speed as output. Four typical artificial neural network models (BP network,RBF network, Elman network and GA-BP network) were described in the paper. From theanalysis results between optimization results and actual value, the ideal optimization modelwas selected.
The rotor speed needs to be changed according to the changes of working conditionsin the process of oil production. In order to realize the real-time control for speed, a PCPspeed optimization system was developed. The development environment of hardware andsoftware, algorithm in the software, measurement and real-time control for PCP speed andits influence factors utilizing this system were introduced in the paper.
In order to verify the effectiveness of PCP speed optimization model and make furtherstudy on speed optimization, a test platform of PCP speed optimization which can simulatethe actual well conditions was self-designed and developed. The main structure of the testplatform was ring-block type wear testing machine and combining with the developedspeed optimization system, it can complete the effectiveness verification of PCP speedoptimization model and the experimental research for different control modes.
On the test platform, PCP speed optimization results obtained by artificial neuralnetwork optimization model were analyzed in experiment. Experiments with differentschemes were carried on, and from the results it can be seen that the wear loss of statorrubber was obviously reduced with the speed which is optimized by the BP neural networkoptimization model.
Finally, this paper proposed a constructive idea to improve PCP speed optimizationplatform based on artificial neural network. Moreover, it gave the open architecture ofoptimization system and the subsystem interface between actual signal acquisitions.
论文首先分析了螺杆泵结构和工作原理，综述了螺杆泵采油技术的国内外发展状况，重点对螺杆泵采油技术领域中存在的关键技术问题进行了剖析，确定了论文工作的切入点。
速度是影响螺杆泵定子橡胶寿命的主导因素，研究他们之间的关系正是论文工作的核心。但是速度对橡胶磨损的影响又受制于螺杆泵结构参数和油井工况参数，这种影响呈非线性耦合形态。对此，文中在梳理了两大类影响因素的基础上，对解决非线性耦合关系问题的方法进行了考证，提出基于人工神经网络技术建立螺杆泵转速优化模型的思路，选取原油温度、原油粘度、螺杆泵泵端压差和螺杆泵定子橡胶磨损量作为输入量，以螺杆泵转速作为输出量，模拟实际工况并以其为参考考察优化效果。文中阐述了基于四种典型的人工神经网络模型（BP网络、RBF网络、Elman网络和GA-BP网络）进行优化的具体算法及优化结果与实际值的对比分析过程，从中遴选了比较理想的优化模型。
在螺杆泵采油系统的实际工作过程中，转速需根据实际工况的变化而变化。为实现螺杆泵转速的实时控制，自行开发了螺杆泵转速优化系统。文中介绍了软硬件开发环境、软件中内含的算法以及基于此平台对螺杆泵转速及其影响因素的测量和实时调控等操作过程。
为验证螺杆泵转速优化模型的有效性，并对转速优化问题深入研究，自行设计并研制了能够模拟实际油井工况的螺杆泵转速优化结果检验平台。该实验平台以环-块式摩擦磨损试验机为主体，结合所开发的螺杆泵转速优化系统，能够完成螺杆泵转速优化模型实效性的验证以及不同控制方式实效性的试验研究。
在螺杆泵转速优化效果检验平台上，对基于人工神经网络优化模型获得的螺杆泵转速优化结果进行了实验分析。采用不同试验方案（橡胶配方）进行了试验，对比分析实验结果可以看出，基于BP神经网络优化模型优化出的转速对减轻螺杆泵定子橡胶磨损量的效果比较明显。
最后，对进一步完善基于人工神经网络的螺杆泵转速优化平台提出了建设性思路，给出了优化系统的开放性架构和与实际信号采集子系统的接口。
As a new artificial lift method, progressing cavity pump (PCP) oil production hasbeen widely used in the oil fields at home and abroad for its characteristics includingsimple structure, high efficiency energy saving, small floor area and so on. PCP isespecially suitable for the oil well of production high viscosity oil, high sand content andhigh gas content. The choice of rotor speed of PCP speed is very important in the oilproduction; the rotor speed will directly affect the pump efficiency and service life. If theselection of rotor speed is unreasonable, it will cause “burn pump”, lower pump efficiency,shorten service life and other problems. Recently, researchers at home and abroad haveused the method of establishing the best speed model to choose reasonable speed. Thedisadvantage of the best speed model is that it does not consider the coupling influence ofgeometric parameters and structure parameters, and the convergence speed of establishedmodel is slower and accuracy is lower. Meanwhile, the researchers have not further studyin the problem of speed intelligent control, and the effect is still not obvious to improvepump efficiency and prolong life. Therefore, this paper studied PCP speed optimizationmethod from the theory and experiment both aspects, and trying to improve the level ofPCP speed control technology.
In the first place, the PCP structure and its working principle were analyzed in thearticle. Then, development of the PCP technology both at home and abroad wassummarized. The key problems of oil production technology of PCP were deeply discussedand the breakthrough point of this work was determined.
Speed is the dominant factor of affecting the service life of PCP stator rubber;therefore the core of this paper was study the relationship between them. However, theinfluence of speed on rubber wear depends on structure parameters and working conditionparameters and the influence shows a nonlinear coupling relationship. Therefore, on thebasis of combing the two groups of influence factors, the paper made a textual research forthe method of solving nonlinear coupling. The thought of establishing optimization model of PCP speed on the basis of artificial neural network was proposed. In order to simulatethe actual working condition and investigate optimization effect, the oil temperature, oilviscosity, pressure difference of pump ends and wear loss of stator rubber were selected asinput, and PCP speed as output. Four typical artificial neural network models (BP network,RBF network, Elman network and GA-BP network) were described in the paper. From theanalysis results between optimization results and actual value, the ideal optimization modelwas selected.
The rotor speed needs to be changed according to the changes of working conditionsin the process of oil production. In order to realize the real-time control for speed, a PCPspeed optimization system was developed. The development environment of hardware andsoftware, algorithm in the software, measurement and real-time control for PCP speed andits influence factors utilizing this system were introduced in the paper.
In order to verify the effectiveness of PCP speed optimization model and make furtherstudy on speed optimization, a test platform of PCP speed optimization which can simulatethe actual well conditions was self-designed and developed. The main structure of the testplatform was ring-block type wear testing machine and combining with the developedspeed optimization system, it can complete the effectiveness verification of PCP speedoptimization model and the experimental research for different control modes.
On the test platform, PCP speed optimization results obtained by artificial neuralnetwork optimization model were analyzed in experiment. Experiments with differentschemes were carried on, and from the results it can be seen that the wear loss of statorrubber was obviously reduced with the speed which is optimized by the BP neural networkoptimization model.
Finally, this paper proposed a constructive idea to improve PCP speed optimizationplatform based on artificial neural network. Moreover, it gave the open architecture ofoptimization system and the subsystem interface between actual signal acquisitions.
引文
[1]孙喜寿.机械采油方法的正确选择[J].国外油田工程，1994，(3)：13~14.
[2]冯耀忠.油井机械采油方法的选择[J].石油机械，1989，17(12)：14~18.
[3] Clegg J.D, Bucaram S.M, Hein Jr N.W. Recommendations and comparisons for selectingartificial-lift methods[J]. Journal of Petroleum Technology,1993,45(12):1128~1131.
[4] Kahali K, Rai R, Mukerjie R K. Artificial lift methods for marginal fields. Proc SPE Prod OperSymp,1991,4:597~605.
[5]张连山.螺杆泵采油系统技术发展现状与动向研究[J].石油机械，1994，22(1)：46~50.
[6] Beauquin J.L, Boireau C, Lemay L, Seince L. Development status of a metal progressing cavitypump for heavy-oil and hot-production wells[J]. Journal of Petroleum Technology,2006,58(5):59~61.
[7] Klein Steven. Development of composite progressing cavity pumps[J]. Proceedings of the AnnualSouthwestern Petroleum Short Course,2003,4:74~78.
[8]黄有泉，何艳，曹刚.大庆油田螺杆泵采油技术新进展[J].石油机械，2003，31(11)：65~69.
[9]王世杰，李勤.潜油螺杆泵采油技术及系统设计[M].北京：冶金工业出版社，2006.
[10]张军，陈听宽，金友煌.螺杆泵转速选择应考虑的几个问题[J].石油钻采工艺，1998，20(2)：88~90.
[11]胡茂军，王军，高原.螺杆泵井合理转速的确定与应用[J].油气田地面工程，2009，28(11)：38~39.
[12] Wang Shijie, Sun Shuhui, Zhang Bingyi. Study of multi-port on-line monitoring technology ofelectrical submersible progressing cavity pumping operation[C]. Proceeding of the6thInternational Conference on Frontiers of Design and Manufacturing,2004.
[13]师国臣，徐桂艳，张颖等.转速对螺杆泵工作特性影响的试验研究[J].石油机械，2000，28：66~67.
[14]万邦烈.单螺杆式水力机械[M].山东：石油大学出版社，1993.
[15]金红杰，吴恒安，曹刚等.螺杆泵系统漏失和磨损机理研究[J].工程力学，2010，27(4)：179~184.
[16]魏纪德.螺杆泵工作特性研究及应用[D]：(博士学位论文).黑龙江：大庆石油学院，2007.
[17]申亮.地面驱动螺杆泵工况诊断技术研究[D]：(硕士学位论文).北京：中国石油大学，2011.
[18]熊希.地面驱动螺杆泵采油系统优化设计[D]：(硕士学位论文).湖北：长江大学，2012.
[19]管延收.电潜螺杆泵采油系统的理论研究与应用分析[D]：(硕士学位论文).北京：中国石油大学，2008.
[20]孔倩倩.电动潜油螺杆泵工况诊断方法研究[D]：(硕士学位论文).北京：中国石油大学，2008.
[21]齐振林.螺杆泵采油技术问答[M].北京：石油工业出版社，2002.
[22]廖开贵，李允，陈次昌.采油螺杆泵研发新进展[J].国外油田工程，2006，22(10)：41~43.
[23]曹刚，刘合，黄有泉等.国外螺杆泵举升工艺的新进展[J].石油机械，2004，32(3)：54~55.
[24] Mills R A. Progressing cavity oil well pump-past, present and future[J]. The Journal of CanadianPetroleum Technology,1994,33(4):5~6.
[25] Klein S. Advances expand application or progressive cavity pumps[J]. The American Oil&GasReporter,1995,38(6):83~85.
[26] Wright D W and Adair R L. Progressive cavity pumps deliver highest mechanical efficiency/lowestoperating cost in mature Permian Basin waterflood [J]. Production Operations Symposium,1993,(3):123~130.
[27] James F L, Herald W. What’s new in artificial lift. Part1-Fourteen new systems for beam,progressing-cavity, plunger-lift pumping and gas lift[J]. World Oil,2003,224(4):75.
[28] James F L, Herald W, Robert E S. What’s new in artificial lift. Part1-Fourteen new systems forbeam, progressing-cavity, plunger-lift pumping and gas lift[J]. World Oil,2004,225(4):66~68.
[29] Lea James F, Winkler Herald W, Snyde Robert E. What’s new in artificial lift[J]. World Oil.2007,228(5):59~67.
[30] Lea James F, Winkler Herald W. What’s new in artificial lift[J]. World Oil.2009,230(5):77~85.
[31] Lea James F, Winkler Herald W. What’s new in artificial lift[J]. World Oil.2005,226(5):31~32.
[32] Lea James F, Winkler Herald W. What’s new in artificial lift[J]. World Oil.2005,226(5):35~36.
[33]何艳，魏纪德，李巍等.采油等壁厚定子螺杆泵[P].中国，实用新型专利，200520021785.7.2005.
[34]褚楔军.一种等壁厚空心螺杆泵转子[P].中国，发明专利，200710023428.8.2007.
[35]辽宁华孚石油高科技股份有限公司.耐高温螺杆泵采油装置[P].中国，实用新型专利，200820117624.1.2008.
[36]梁丙雪.潜油螺杆泵专用稀土永磁同步电动机研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2008.
[37]王欣欣.潜油螺杆泵直驱永磁电动机的研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2012.
[38]王世杰，李福宝，孙书会等.潜油螺杆泵采油作业在线监控技术研究[J].中国机械工程，2004，15(14)：1276~1279.
[39]李艺.潜油螺杆泵变频驱动装置的硬件开发[D]：(硕士学位论文).北京：中国石油大学，2007.
[40]王文会.潜油螺杆泵的变频驱动仿真及交交变频器的设计[D]：(硕士学位论文).北京：中国石油大学，2007.
[41]王磊.基于DSP的潜油螺杆泵变频控制技术的实现[D]：(硕士学位论文).北京：中国石油大学，2007.
[42]刘铭.基于大系统思想的潜油螺杆泵采油机组整体优化方法研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2009.
[43] http://www.ep-solutions.com/Solutions/IDM_PCP.htm.Progressing Cavity Pump (PCP) Solution.
[44] http://www.bakerhughes.com/products-and-services/completions-and-productions/artif.
[45] http://www.phoenix-uk.com/pcp_sensors.htm: PCP multi sensory system.
[46]李聪，王世杰，方芳等.基于ANN的ESPCP系统转子转速调控技术研究.机电产品开发与创新[J].2005，18(z1)：102~104.
[47]吴春兰.潜油螺杆泵采油系统转子转速的机电调控技术研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2005.
[48]沈立伟.多因素耦合作用下ESPCP系统控制方法研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2009.
[49]蔡自兴，徐光佑.人工智能及其应用[M].北京：清华大学出版社，2003.
[50]廉师友.人工智能技术导论[M].西安：西安电子科技大学出版社，2007.
[51]张国英，何元娇.人工智能知识体系及学科综述[J].计算机教育，2010，8：25~28.
[52]潘登.建筑结构人工智能实验分析环境[D]：(博士学位论文).黑龙江：哈尔滨工业大学，2011.
[53]罗敏.电力系统等值的人工智能方法的研究[D]：(硕士学位论文).上海：上海交通大学，2010.
[54] Carvalho P G, Morooka C, Bordalo S, Guillermo. CONTROL: PCP-an intelligent system forprogressing cavity pumps[J]. Proceedings-SPE Annual Technical Conference and Exhibition,2000,PI:421~428.
[55]盛国富.螺杆泵自调式控制器[J].国外油田工程，2005，21(1)：46.
[56]李敏，何平，孟臣.螺杆泵井智能集成故障诊断专家系统研究[J].哈尔滨工业大学学报，2010，42(7)：1038~1041.
[57]杨樟柏.提高螺杆泵举升性能研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2006.
[58] Bybee Karen. New approach for modeling progressing-cavity-pump performance[J]. Journal ofPetroleum Technology,2004,56(5):56~57.
[59] Meng Bixia, Wen Houzhen. The performance indexes and the influence factor of PCP[J]. AppliedMechanics and Materials,2012,229-231:347~350.
[60] Gamboa Jose, Olivet Aurello, Espin Sorelys. New approach for modeling progressive cavity pumpsperformance[J]. Proceedings-SPE Annual Technical Conference and Exhibition,2003,807~815.
[61]郁文正.地面驱动采油螺杆泵设计中的若干问题[J].石油机械，1992，20(6)：5~10.
[62]付亚荣，陈善峰.地面驱动采油螺杆泵工作制度的确定[J].新疆石油学院学报，2000，12(2)：38~42.
[63] Wang Shijie, Sun Hao. Research of real-time monitoring technology for progressing cavitypumping operation[J]. Applied Mechanics and Materials,2011,44-47:4094~4099.
[64]王永昌，郑贵，胡景新.螺杆泵试验转速和黏度对水力特性检测的影响[J].石油工业技术监督，2009，(7)：5~9.
[65]韩修廷，王秀玲.螺杆泵采油原理及应用[M].黑龙江：哈尔滨工程大学出版社，1998.
[66]杜香芝.螺杆泵检测系统[J].油气田工程，2007，26(5)：62.
[67]王永昌，杜香芝.螺杆泵试验转速和温度对水力特性的影响[J].石油矿场机械，2011，40(4)：65~69.
[68]李迎新.螺杆泵井工况诊断及杆柱优化设计方法研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2006.
[69] Wilson B.L, Pankratz R.E. Predicting power cost and its role in ESP economics[J]. Artificial liftWorkshop,1987,(4):22~24.
[70] Vandevier J.E. Optimum power cable sizing for electric submersible pumps[J]. ProductionOperations Symposium,1987,(3):8~10.
[71]王春艳.螺杆泵抽油井工况分析[D]：(硕士学位论文).黑龙江：大庆石油学院，2004.
[72] Savins J G, Wallick G C. Viscosity profiles, discharge rates, pressures and torques for atheologically complex fluid in a helical flow[J]. A.I.Ch. Journal,1996,26(5):22~25.
[73]王志伟，栾溪，张宁生.螺杆泵采油井优化设计软件开发与应用[J].石油工业计算机应用，2005，13(4)：17~18.
[74]赵殿勇.螺杆泵井转速对泵效的影响研究与应用[J].中外能源，2012，17：64~67.
[75] Zhou Desheng, Yuan Hong. Design of Progressive Cavity Pump wells[J]. Society of PetroleumEngineers-Progressing Cavity Pumps Conference2008,2008:36~47.
[76]王国庆，师国臣，马志权等.大庆油田螺杆泵机采井系统效率现状及对策[J].石油矿场机械，2011，40(7)：25~28.
[77]武云石.提高螺杆泵井系统效率的措施研究[J].油气田地面工程，2009，28(7)：7~8.
[78]魏纪德，师国臣.试验介质温度、粘度对螺杆泵容积效率的影响[J].石油机械，1993，21(9)：15~20.
[79]李萍，陈勇.油田螺杆泵定子橡胶性能的影响因素[J].橡胶科技市场，2008，(13)：23~25.
[80]杨秀萍，郭津津.单螺杆泵定子橡胶的接触磨损分析[J].润滑与密封，2007，32(4)：33~35.
[81]陈玉祥，王霞，周松等.提高螺杆泵定子橡胶材料寿命的分析与研究[J].排灌机械，2005，23(4)：6~9.
[82]何艳.螺杆泵采油系统优化延长检泵周期技术研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2006.
[83]侯宇.螺杆泵定转子合理过盈量确定方法研究[D]：(硕士学位论文).黑龙江：东北石油大学，2011.
[84]吕彪.螺杆泵合理过盈量研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2008.
[85] Zhang Shiwei, Zhang Zhijun, Xu Chenghai. Virtual design and structural optimization of dry twinscrew vacuum pump with a new rotor profile[J]. Applied Mechanics and Materials,2009,16-19:1392~1396.
[86]叶卫东，韩道权，宋玉杰等.螺杆泵定子与转子的接触分析[J].石油矿场机械，2008，37(8)：25~28.
[87]朱大奇.人工神经网络研究现状及其展望[J].江南大学学报(自然科学版)，2004，3(1)：103~109.
[88]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社，2002.
[89] Ding Shifei, Jia Weikuan, Su Chunyang, etc. Neural network research progress and applications inforecast[J]. Lecture Notes in Computer Science,2008,5264(PART2):783~793.
[90]张治国.人工神经网络及其在地学中的应用研究[D]：(博士学位论文).吉林：吉林大学，2006.
[91]覃光华.人工神经网络技术及其应用[D]：(博士学位论文).四川：四川大学，2003.
[92]白艳萍.人工神经网络在组合优化与信息处理中的应用[D]：(博士学位论文).山西：中北大学，2005.
[93]林开平.人工神经网络的泛化性能与降水预报的应用研究[D]：(博士学位论文).江苏：南京信息工程大学，2007.
[94]张丽.基于神经网络的仿真元建模方法研究[D]：(硕士学位论文).湖南：国防科学技术大学，2003.
[95]王长会.热轧带钢层流冷却过程控制方法的应用研究[D]：(硕士学位论文).辽宁：东北大学，2005.
[96] Rumelhart D E. Learning Representation by BP Errors[J]. Nature,1986,(7):64~70.
[97] Patrick P. Minimisation Method for Training Feed forward Neural Network[J]. Neural Network,1994,(7):145~163.
[98]李天军.RBF神经网络及其在锅炉过热汽温控制中的应用[D]：(硕士学位论文).黑龙江：哈尔滨工业大学，2007.
[99] Powell M J D. Radial basis function for multivariable interpolations: a review[J]. In IMAConference on Algorithms for the Approximation of Functions and Data. RMCS, Shrivenham UK,1985,143~167.
[100]郑明文.径向基神经网络训练算法及其性能研究[D]：(硕士学位论文).北京：中国石油大学，2009.
[101] Powell M J D. Radial basis function approximations to polynomials[J]. Numerical Analysisproceedings,1987:223~241.
[102]任丽娜.基于Elman神经网络的中期电力负荷预测模型研究[D]：(硕士学位论文).甘肃：兰州理工大学，2007.
[103] Pham D T, X Liu. Dynamic system modeling using partially recurrent neural networks[J]. Journalof Systems Engineering,1992,2:90~97.
[104]时小虎.Elman神经网络与进化算法的若干理论研究及应用[D]：(博士学位论文).吉林：吉林大学，2006.
[105] Gao Guodong, Zhang Wenxiao, Sui Jianghua, etc. Research on Diesel Engine Fault DiagnosisModeling Based on Elman Neural Network[J]. Advanced Materials Research,2012,361-363(pt.3):1506~1509.
[106]景广军，梁雪梅，范训礼.遗传神经网络预测模型的设计及应用[J].计算机工程与应用，2001，(9)：1~3.
[107]刘洁，魏连雨，杨春风.基于遗传-神经网络的交通量预测[J].长安大学学报（自然科学版），2003，23(1)：68~70.
[108]王小平，曹立明.遗传算法-理论、应用与软件实现[M].陕西：西安交通大学出版社，2002.
[109]吕砚山，赵正琦.BP神经网络的优化及应用研究[J].北京化工大学学报，2001，28(1)：67~69.
[110]郭雨梅，关蕊，钟媛.基于径向基神经网络的光栅细分方法[J].沈阳工业大学学报，2011，33(2)：193~197.
[111] Tan K K, Huang S N, Seet H L. Geometrical error compensation of precision motion systemsusing Radial basis function[J]. IEEE Instrumentation and Measurement,2000,49(5):984~991.
[112] Khairnar, D.G.; Merchant, S.N.; Desai, U.B. Radar signal detection in non-Gaussian noise usingRBF neural network[J]. Journal of Computers,2008,3(1):32~39.
[113]欧海涛，张文渊，杨煜普等.高速公路交通流的RBF神经网络建模[J].上海交通大学学报，2000，34(5)：665~668.
[114]张慧斌，高秀萍.基于Elman神经网络的时间序列股票价格短期预测[J].山西大同大学学报(自然科学版)，2011，27(2)：5~7.
[115]李界家，郭宏伟，文达.基于铝电解过程神经网络建模的研究[J].沈阳建筑大学学报(自然科学版)，2007，23(2)：341~344.
[116] Qi Zhong, Liu Mandan, Wang Honggang. Process modeling method based on an improved Elmanneural network.20087th World Congress on Intelligent Control and Automation,2008,8188~92.
[117]姚宏善，沈轶.用遗传神经网络模型预测公司财务困境[J].华中师范大学学报(自然科学版)，2005，39(2)：195~197.
[118]牛东晓，陈志业，邢棉等.遗传神经网络优化预测方法研究及其应用[J].华北电力大学学报，2001，28(1)：1~5.
[119] Li Xingpei, Liu Yibing, Xin Weidong. Wind speed prediction based on genetic neural network[J].20094th IEEE Conference on Industrial Electronics and Applications,2009,2448~2451.
[120] Wen Lei, Qiu Zhiwen, Qi Ruonan. Passenger capacity prediction based on genetic neuralnetwork[J].2009International Symposium on Information Engineering and Electronic Commerce(IEEC),2009,696~700.
[121] Mohammadi Azadeh, Ashouri Mohammad R. Hybrid neural network-genetic algorithm method topredict monthly minimum and maximum of stock prices[J]. Proceedings of the2008InternationalConference on Artificial Intelligence and Proceedings of the2008International Conference onMachine Learning, Models, Technologies and Applications, ICAI2008,2:794~799.
[122]李俊平，张立，薛海燕.Delphi程序设计与软件项目开发[M].北京：清华大学出版社，2007.
[123]张增强，武向辉.Delphi6入门与提高[M].北京：人民邮电出版社，2002.
[124]张德丰.MATLAB神经网络应用设计[M].北京：机械工业出版社，2009.
[125]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2005.
[126]施阳，李俊.MATLAB语言工具箱-TOOLBOX实用指南[M].陕西：西北工业大学出版社，1999.
[127]张德丰.MATLAB与外部程序接口编程[M].北京：机械工业出版社，2009.
[128]胡劲松，周方洁.基于COM的MATLAB与Delphi混合编程研究[J].计算机应用研究，2005，(1)：165~166.
[129] Jun Wang, Chen Jianyun, Yan Zhuang. Mixed programming between MATLAB and otherprogramming languages[J]. Communications in Computer and Information Science,2011,225(PART2):669~676.
[130] Bian Xiaolin, Wang Jinbo. MATLAB COM Component in Radar Signal Processing ApplicationDevelopment[J]. Proceedings20103rd International Conference on Information Management,Innovation Management and Industrial Engineering (ICIII2010),2010,397~399.
[131] Sun Miaozhong, Wang Chuan, Xu Yuanli. Fault diagnosis of grinding machine usingChoi-Williams distribution based on com module technology[J]. Applied Mechanics and Materials,2012,226-228:572~575.
[132]宁萌.海洋水文站环境条件数据库开发及工程应用[D]：(硕士学位论文).山东：中国海洋大学，2007.
[133]倪琲.MHK-500环块摩擦磨损试验机智能测试系统的研制[D]：(硕士学位论文).安徽：合肥工业大学，2007.
[134]袁细传.振动载荷磨损试验机的研制及材料磨损性能研究[D]：(硕士学位论文).湖南：湘潭大学，2007.
[135]吴明晖，袁细传，谭元强等.基于虚拟仪器技术的多功能摩擦磨损试验机的研制[J].润滑与密封，2010，35(6)：96~100.
[136] Mengistu S, Bingley M S, Bradley M S A. Frictional characteristics of steel plates during abrasiveparticle flow: a comparison of in situ measurements made on a linear abrasive wear tester withthose on a Jenike shear tester[J]. Proceedings of the Institution of Mechanical Engineers, Part E,2004,218(E4):221~235.
[137] Wiche S J, Keys S, Roberts A W. Abrasion wear tester for bulk solids handling applications[J].Wear,2005,258(1-4):251~257.
[138] He Da Hai, Manory Rafael, Sinkis Harry. A sliding wear tester for overhead wires and currentcollectors in light rail systems[J]. Wear,2000,239(1):10~20.
[2]冯耀忠.油井机械采油方法的选择[J].石油机械，1989，17(12)：14~18.
[3] Clegg J.D, Bucaram S.M, Hein Jr N.W. Recommendations and comparisons for selectingartificial-lift methods[J]. Journal of Petroleum Technology,1993,45(12):1128~1131.
[4] Kahali K, Rai R, Mukerjie R K. Artificial lift methods for marginal fields. Proc SPE Prod OperSymp,1991,4:597~605.
[5]张连山.螺杆泵采油系统技术发展现状与动向研究[J].石油机械，1994，22(1)：46~50.
[6] Beauquin J.L, Boireau C, Lemay L, Seince L. Development status of a metal progressing cavitypump for heavy-oil and hot-production wells[J]. Journal of Petroleum Technology,2006,58(5):59~61.
[7] Klein Steven. Development of composite progressing cavity pumps[J]. Proceedings of the AnnualSouthwestern Petroleum Short Course,2003,4:74~78.
[8]黄有泉，何艳，曹刚.大庆油田螺杆泵采油技术新进展[J].石油机械，2003，31(11)：65~69.
[9]王世杰，李勤.潜油螺杆泵采油技术及系统设计[M].北京：冶金工业出版社，2006.
[10]张军，陈听宽，金友煌.螺杆泵转速选择应考虑的几个问题[J].石油钻采工艺，1998，20(2)：88~90.
[11]胡茂军，王军，高原.螺杆泵井合理转速的确定与应用[J].油气田地面工程，2009，28(11)：38~39.
[12] Wang Shijie, Sun Shuhui, Zhang Bingyi. Study of multi-port on-line monitoring technology ofelectrical submersible progressing cavity pumping operation[C]. Proceeding of the6thInternational Conference on Frontiers of Design and Manufacturing,2004.
[13]师国臣，徐桂艳，张颖等.转速对螺杆泵工作特性影响的试验研究[J].石油机械，2000，28：66~67.
[14]万邦烈.单螺杆式水力机械[M].山东：石油大学出版社，1993.
[15]金红杰，吴恒安，曹刚等.螺杆泵系统漏失和磨损机理研究[J].工程力学，2010，27(4)：179~184.
[16]魏纪德.螺杆泵工作特性研究及应用[D]：(博士学位论文).黑龙江：大庆石油学院，2007.
[17]申亮.地面驱动螺杆泵工况诊断技术研究[D]：(硕士学位论文).北京：中国石油大学，2011.
[18]熊希.地面驱动螺杆泵采油系统优化设计[D]：(硕士学位论文).湖北：长江大学，2012.
[19]管延收.电潜螺杆泵采油系统的理论研究与应用分析[D]：(硕士学位论文).北京：中国石油大学，2008.
[20]孔倩倩.电动潜油螺杆泵工况诊断方法研究[D]：(硕士学位论文).北京：中国石油大学，2008.
[21]齐振林.螺杆泵采油技术问答[M].北京：石油工业出版社，2002.
[22]廖开贵，李允，陈次昌.采油螺杆泵研发新进展[J].国外油田工程，2006，22(10)：41~43.
[23]曹刚，刘合，黄有泉等.国外螺杆泵举升工艺的新进展[J].石油机械，2004，32(3)：54~55.
[24] Mills R A. Progressing cavity oil well pump-past, present and future[J]. The Journal of CanadianPetroleum Technology,1994,33(4):5~6.
[25] Klein S. Advances expand application or progressive cavity pumps[J]. The American Oil&GasReporter,1995,38(6):83~85.
[26] Wright D W and Adair R L. Progressive cavity pumps deliver highest mechanical efficiency/lowestoperating cost in mature Permian Basin waterflood [J]. Production Operations Symposium,1993,(3):123~130.
[27] James F L, Herald W. What’s new in artificial lift. Part1-Fourteen new systems for beam,progressing-cavity, plunger-lift pumping and gas lift[J]. World Oil,2003,224(4):75.
[28] James F L, Herald W, Robert E S. What’s new in artificial lift. Part1-Fourteen new systems forbeam, progressing-cavity, plunger-lift pumping and gas lift[J]. World Oil,2004,225(4):66~68.
[29] Lea James F, Winkler Herald W, Snyde Robert E. What’s new in artificial lift[J]. World Oil.2007,228(5):59~67.
[30] Lea James F, Winkler Herald W. What’s new in artificial lift[J]. World Oil.2009,230(5):77~85.
[31] Lea James F, Winkler Herald W. What’s new in artificial lift[J]. World Oil.2005,226(5):31~32.
[32] Lea James F, Winkler Herald W. What’s new in artificial lift[J]. World Oil.2005,226(5):35~36.
[33]何艳，魏纪德，李巍等.采油等壁厚定子螺杆泵[P].中国，实用新型专利，200520021785.7.2005.
[34]褚楔军.一种等壁厚空心螺杆泵转子[P].中国，发明专利，200710023428.8.2007.
[35]辽宁华孚石油高科技股份有限公司.耐高温螺杆泵采油装置[P].中国，实用新型专利，200820117624.1.2008.
[36]梁丙雪.潜油螺杆泵专用稀土永磁同步电动机研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2008.
[37]王欣欣.潜油螺杆泵直驱永磁电动机的研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2012.
[38]王世杰，李福宝，孙书会等.潜油螺杆泵采油作业在线监控技术研究[J].中国机械工程，2004，15(14)：1276~1279.
[39]李艺.潜油螺杆泵变频驱动装置的硬件开发[D]：(硕士学位论文).北京：中国石油大学，2007.
[40]王文会.潜油螺杆泵的变频驱动仿真及交交变频器的设计[D]：(硕士学位论文).北京：中国石油大学，2007.
[41]王磊.基于DSP的潜油螺杆泵变频控制技术的实现[D]：(硕士学位论文).北京：中国石油大学，2007.
[42]刘铭.基于大系统思想的潜油螺杆泵采油机组整体优化方法研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2009.
[43] http://www.ep-solutions.com/Solutions/IDM_PCP.htm.Progressing Cavity Pump (PCP) Solution.
[44] http://www.bakerhughes.com/products-and-services/completions-and-productions/artif.
[45] http://www.phoenix-uk.com/pcp_sensors.htm: PCP multi sensory system.
[46]李聪，王世杰，方芳等.基于ANN的ESPCP系统转子转速调控技术研究.机电产品开发与创新[J].2005，18(z1)：102~104.
[47]吴春兰.潜油螺杆泵采油系统转子转速的机电调控技术研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2005.
[48]沈立伟.多因素耦合作用下ESPCP系统控制方法研究[D]：(硕士学位论文).沈阳：沈阳工业大学，2009.
[49]蔡自兴，徐光佑.人工智能及其应用[M].北京：清华大学出版社，2003.
[50]廉师友.人工智能技术导论[M].西安：西安电子科技大学出版社，2007.
[51]张国英，何元娇.人工智能知识体系及学科综述[J].计算机教育，2010，8：25~28.
[52]潘登.建筑结构人工智能实验分析环境[D]：(博士学位论文).黑龙江：哈尔滨工业大学，2011.
[53]罗敏.电力系统等值的人工智能方法的研究[D]：(硕士学位论文).上海：上海交通大学，2010.
[54] Carvalho P G, Morooka C, Bordalo S, Guillermo. CONTROL: PCP-an intelligent system forprogressing cavity pumps[J]. Proceedings-SPE Annual Technical Conference and Exhibition,2000,PI:421~428.
[55]盛国富.螺杆泵自调式控制器[J].国外油田工程，2005，21(1)：46.
[56]李敏，何平，孟臣.螺杆泵井智能集成故障诊断专家系统研究[J].哈尔滨工业大学学报，2010，42(7)：1038~1041.
[57]杨樟柏.提高螺杆泵举升性能研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2006.
[58] Bybee Karen. New approach for modeling progressing-cavity-pump performance[J]. Journal ofPetroleum Technology,2004,56(5):56~57.
[59] Meng Bixia, Wen Houzhen. The performance indexes and the influence factor of PCP[J]. AppliedMechanics and Materials,2012,229-231:347~350.
[60] Gamboa Jose, Olivet Aurello, Espin Sorelys. New approach for modeling progressive cavity pumpsperformance[J]. Proceedings-SPE Annual Technical Conference and Exhibition,2003,807~815.
[61]郁文正.地面驱动采油螺杆泵设计中的若干问题[J].石油机械，1992，20(6)：5~10.
[62]付亚荣，陈善峰.地面驱动采油螺杆泵工作制度的确定[J].新疆石油学院学报，2000，12(2)：38~42.
[63] Wang Shijie, Sun Hao. Research of real-time monitoring technology for progressing cavitypumping operation[J]. Applied Mechanics and Materials,2011,44-47:4094~4099.
[64]王永昌，郑贵，胡景新.螺杆泵试验转速和黏度对水力特性检测的影响[J].石油工业技术监督，2009，(7)：5~9.
[65]韩修廷，王秀玲.螺杆泵采油原理及应用[M].黑龙江：哈尔滨工程大学出版社，1998.
[66]杜香芝.螺杆泵检测系统[J].油气田工程，2007，26(5)：62.
[67]王永昌，杜香芝.螺杆泵试验转速和温度对水力特性的影响[J].石油矿场机械，2011，40(4)：65~69.
[68]李迎新.螺杆泵井工况诊断及杆柱优化设计方法研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2006.
[69] Wilson B.L, Pankratz R.E. Predicting power cost and its role in ESP economics[J]. Artificial liftWorkshop,1987,(4):22~24.
[70] Vandevier J.E. Optimum power cable sizing for electric submersible pumps[J]. ProductionOperations Symposium,1987,(3):8~10.
[71]王春艳.螺杆泵抽油井工况分析[D]：(硕士学位论文).黑龙江：大庆石油学院，2004.
[72] Savins J G, Wallick G C. Viscosity profiles, discharge rates, pressures and torques for atheologically complex fluid in a helical flow[J]. A.I.Ch. Journal,1996,26(5):22~25.
[73]王志伟，栾溪，张宁生.螺杆泵采油井优化设计软件开发与应用[J].石油工业计算机应用，2005，13(4)：17~18.
[74]赵殿勇.螺杆泵井转速对泵效的影响研究与应用[J].中外能源，2012，17：64~67.
[75] Zhou Desheng, Yuan Hong. Design of Progressive Cavity Pump wells[J]. Society of PetroleumEngineers-Progressing Cavity Pumps Conference2008,2008:36~47.
[76]王国庆，师国臣，马志权等.大庆油田螺杆泵机采井系统效率现状及对策[J].石油矿场机械，2011，40(7)：25~28.
[77]武云石.提高螺杆泵井系统效率的措施研究[J].油气田地面工程，2009，28(7)：7~8.
[78]魏纪德，师国臣.试验介质温度、粘度对螺杆泵容积效率的影响[J].石油机械，1993，21(9)：15~20.
[79]李萍，陈勇.油田螺杆泵定子橡胶性能的影响因素[J].橡胶科技市场，2008，(13)：23~25.
[80]杨秀萍，郭津津.单螺杆泵定子橡胶的接触磨损分析[J].润滑与密封，2007，32(4)：33~35.
[81]陈玉祥，王霞，周松等.提高螺杆泵定子橡胶材料寿命的分析与研究[J].排灌机械，2005，23(4)：6~9.
[82]何艳.螺杆泵采油系统优化延长检泵周期技术研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2006.
[83]侯宇.螺杆泵定转子合理过盈量确定方法研究[D]：(硕士学位论文).黑龙江：东北石油大学，2011.
[84]吕彪.螺杆泵合理过盈量研究[D]：(硕士学位论文).黑龙江：大庆石油学院，2008.
[85] Zhang Shiwei, Zhang Zhijun, Xu Chenghai. Virtual design and structural optimization of dry twinscrew vacuum pump with a new rotor profile[J]. Applied Mechanics and Materials,2009,16-19:1392~1396.
[86]叶卫东，韩道权，宋玉杰等.螺杆泵定子与转子的接触分析[J].石油矿场机械，2008，37(8)：25~28.
[87]朱大奇.人工神经网络研究现状及其展望[J].江南大学学报(自然科学版)，2004，3(1)：103~109.
[88]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社，2002.
[89] Ding Shifei, Jia Weikuan, Su Chunyang, etc. Neural network research progress and applications inforecast[J]. Lecture Notes in Computer Science,2008,5264(PART2):783~793.
[90]张治国.人工神经网络及其在地学中的应用研究[D]：(博士学位论文).吉林：吉林大学，2006.
[91]覃光华.人工神经网络技术及其应用[D]：(博士学位论文).四川：四川大学，2003.
[92]白艳萍.人工神经网络在组合优化与信息处理中的应用[D]：(博士学位论文).山西：中北大学，2005.
[93]林开平.人工神经网络的泛化性能与降水预报的应用研究[D]：(博士学位论文).江苏：南京信息工程大学，2007.
[94]张丽.基于神经网络的仿真元建模方法研究[D]：(硕士学位论文).湖南：国防科学技术大学，2003.
[95]王长会.热轧带钢层流冷却过程控制方法的应用研究[D]：(硕士学位论文).辽宁：东北大学，2005.
[96] Rumelhart D E. Learning Representation by BP Errors[J]. Nature,1986,(7):64~70.
[97] Patrick P. Minimisation Method for Training Feed forward Neural Network[J]. Neural Network,1994,(7):145~163.
[98]李天军.RBF神经网络及其在锅炉过热汽温控制中的应用[D]：(硕士学位论文).黑龙江：哈尔滨工业大学，2007.
[99] Powell M J D. Radial basis function for multivariable interpolations: a review[J]. In IMAConference on Algorithms for the Approximation of Functions and Data. RMCS, Shrivenham UK,1985,143~167.
[100]郑明文.径向基神经网络训练算法及其性能研究[D]：(硕士学位论文).北京：中国石油大学，2009.
[101] Powell M J D. Radial basis function approximations to polynomials[J]. Numerical Analysisproceedings,1987:223~241.
[102]任丽娜.基于Elman神经网络的中期电力负荷预测模型研究[D]：(硕士学位论文).甘肃：兰州理工大学，2007.
[103] Pham D T, X Liu. Dynamic system modeling using partially recurrent neural networks[J]. Journalof Systems Engineering,1992,2:90~97.
[104]时小虎.Elman神经网络与进化算法的若干理论研究及应用[D]：(博士学位论文).吉林：吉林大学，2006.
[105] Gao Guodong, Zhang Wenxiao, Sui Jianghua, etc. Research on Diesel Engine Fault DiagnosisModeling Based on Elman Neural Network[J]. Advanced Materials Research,2012,361-363(pt.3):1506~1509.
[106]景广军，梁雪梅，范训礼.遗传神经网络预测模型的设计及应用[J].计算机工程与应用，2001，(9)：1~3.
[107]刘洁，魏连雨，杨春风.基于遗传-神经网络的交通量预测[J].长安大学学报（自然科学版），2003，23(1)：68~70.
[108]王小平，曹立明.遗传算法-理论、应用与软件实现[M].陕西：西安交通大学出版社，2002.
[109]吕砚山，赵正琦.BP神经网络的优化及应用研究[J].北京化工大学学报，2001，28(1)：67~69.
[110]郭雨梅，关蕊，钟媛.基于径向基神经网络的光栅细分方法[J].沈阳工业大学学报，2011，33(2)：193~197.
[111] Tan K K, Huang S N, Seet H L. Geometrical error compensation of precision motion systemsusing Radial basis function[J]. IEEE Instrumentation and Measurement,2000,49(5):984~991.
[112] Khairnar, D.G.; Merchant, S.N.; Desai, U.B. Radar signal detection in non-Gaussian noise usingRBF neural network[J]. Journal of Computers,2008,3(1):32~39.
[113]欧海涛，张文渊，杨煜普等.高速公路交通流的RBF神经网络建模[J].上海交通大学学报，2000，34(5)：665~668.
[114]张慧斌，高秀萍.基于Elman神经网络的时间序列股票价格短期预测[J].山西大同大学学报(自然科学版)，2011，27(2)：5~7.
[115]李界家，郭宏伟，文达.基于铝电解过程神经网络建模的研究[J].沈阳建筑大学学报(自然科学版)，2007，23(2)：341~344.
[116] Qi Zhong, Liu Mandan, Wang Honggang. Process modeling method based on an improved Elmanneural network.20087th World Congress on Intelligent Control and Automation,2008,8188~92.
[117]姚宏善，沈轶.用遗传神经网络模型预测公司财务困境[J].华中师范大学学报(自然科学版)，2005，39(2)：195~197.
[118]牛东晓，陈志业，邢棉等.遗传神经网络优化预测方法研究及其应用[J].华北电力大学学报，2001，28(1)：1~5.
[119] Li Xingpei, Liu Yibing, Xin Weidong. Wind speed prediction based on genetic neural network[J].20094th IEEE Conference on Industrial Electronics and Applications,2009,2448~2451.
[120] Wen Lei, Qiu Zhiwen, Qi Ruonan. Passenger capacity prediction based on genetic neuralnetwork[J].2009International Symposium on Information Engineering and Electronic Commerce(IEEC),2009,696~700.
[121] Mohammadi Azadeh, Ashouri Mohammad R. Hybrid neural network-genetic algorithm method topredict monthly minimum and maximum of stock prices[J]. Proceedings of the2008InternationalConference on Artificial Intelligence and Proceedings of the2008International Conference onMachine Learning, Models, Technologies and Applications, ICAI2008,2:794~799.
[122]李俊平，张立，薛海燕.Delphi程序设计与软件项目开发[M].北京：清华大学出版社，2007.
[123]张增强，武向辉.Delphi6入门与提高[M].北京：人民邮电出版社，2002.
[124]张德丰.MATLAB神经网络应用设计[M].北京：机械工业出版社，2009.
[125]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2005.
[126]施阳，李俊.MATLAB语言工具箱-TOOLBOX实用指南[M].陕西：西北工业大学出版社，1999.
[127]张德丰.MATLAB与外部程序接口编程[M].北京：机械工业出版社，2009.
[128]胡劲松，周方洁.基于COM的MATLAB与Delphi混合编程研究[J].计算机应用研究，2005，(1)：165~166.
[129] Jun Wang, Chen Jianyun, Yan Zhuang. Mixed programming between MATLAB and otherprogramming languages[J]. Communications in Computer and Information Science,2011,225(PART2):669~676.
[130] Bian Xiaolin, Wang Jinbo. MATLAB COM Component in Radar Signal Processing ApplicationDevelopment[J]. Proceedings20103rd International Conference on Information Management,Innovation Management and Industrial Engineering (ICIII2010),2010,397~399.
[131] Sun Miaozhong, Wang Chuan, Xu Yuanli. Fault diagnosis of grinding machine usingChoi-Williams distribution based on com module technology[J]. Applied Mechanics and Materials,2012,226-228:572~575.
[132]宁萌.海洋水文站环境条件数据库开发及工程应用[D]：(硕士学位论文).山东：中国海洋大学，2007.
[133]倪琲.MHK-500环块摩擦磨损试验机智能测试系统的研制[D]：(硕士学位论文).安徽：合肥工业大学，2007.
[134]袁细传.振动载荷磨损试验机的研制及材料磨损性能研究[D]：(硕士学位论文).湖南：湘潭大学，2007.
[135]吴明晖，袁细传，谭元强等.基于虚拟仪器技术的多功能摩擦磨损试验机的研制[J].润滑与密封，2010，35(6)：96~100.
[136] Mengistu S, Bingley M S, Bradley M S A. Frictional characteristics of steel plates during abrasiveparticle flow: a comparison of in situ measurements made on a linear abrasive wear tester withthose on a Jenike shear tester[J]. Proceedings of the Institution of Mechanical Engineers, Part E,2004,218(E4):221~235.
[137] Wiche S J, Keys S, Roberts A W. Abrasion wear tester for bulk solids handling applications[J].Wear,2005,258(1-4):251~257.
[138] He Da Hai, Manory Rafael, Sinkis Harry. A sliding wear tester for overhead wires and currentcollectors in light rail systems[J]. Wear,2000,239(1):10~20.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |