摘要
大型民用机场也称为空港,它不仅是交通运输体系中非常重要的组成部分,而且是整个国民经济体系中非常重要的一环,对国民经济的发展具有非常重要的作用。近年来,随着中国经济的快速发展,空港也得到了很大的发展,空港总量初具规模,空港密度逐渐加大,空港服务能力逐步提高,现代化程度不断增强。但是与美国等发达国家相比,空港的整体竞争力仍显不足。以2010年各国旅客周转量的统计数据为例,我国(不含港澳台)空港的定期航班总旅客周转量排名世界第2,仅次于美国,是美国的30.8%；而在国际航线的旅客周转量中排名世界第10,为排名第1的美国的18.6%。虽然有地理位置等客观因素,但是2010年我国(不含港澳台)的国际航线旅客周转量为73488百万人公里,而香港地区的国际航线旅客周转量为85811百万人公里,大于我国全部机场的国际航线旅客周转量!这说明我国空港在国际航线网络中的枢纽地位仍有待提高。提升空港竞争力是中国各空港的必然选择。那么如何识别提取中国空港竞争力的根本型影响因素?中国空港竞争力的动态形成机理是什么?如何评价竞争合作环境下的空港动态竞争力?鉴于以上疑问,本文进行了一系列的深入研究。
首先,对空港竞争力在空港联盟决策中的作用进行了分析。认为众空港在博弈中的收益为不等值,从区域发展、生产要素、需求状况、支撑产业和环境影响五方面建立空港联盟收益的指标体系,将空港网络抽象成小世界网络,结合熵值Topsis和小世界网络博弈动力学建立空港联盟决策算法,并且运用2010年我国25个空港的实证数据进行研究。结果表明,在选取的25个空港中,北京、上海、广州、天津和深圳等国际枢纽空港和大型区域性枢纽空港都选择不联盟策略,且在空港网络博弈稳定后的收益计算中,这些空港的博弈收益仍然是最高的。而一些区域性枢纽空港、区域干线空港及支线空港则都选择联盟策略,这说明相比于大空港,中小空港的联盟积极性更高,但是虽然选择联盟,但是在相互之间的博弈中,仍处于不利位置。这说明空港联盟只能解决一时的问题,空港自身的竞争力仍是主导空港未来发展的根本性因素。
其次,提取空港竞争力的根本型影响因素。空港竞争力影响因素众多且相互关联,如何识别和区分其影响因素是学术界研究的焦点。针对传统决策试验与评价实验室方法(DEMATEL)的缺点进行了改进,提出了适合于影响因素识别的BP-DEMATEL方法,利用BP神经网络计算目标指标和影响因素指标之间的权值来得到直接关联矩阵,然后利用传统DEMATEL方法分析影响因素。本文利用BP-DEMATEL和空港实证数据进行了实证分析,结果证实了方法的可行性。结果表明,影响空港竞争力的原因型因素为GDP、高等学校毕业生数、年投资额、旅客吞吐量、城市居民交通支出、服务半径和公路、铁路、水路总客运量等7个指标,而对空港竞争力影响最大的3个因素是旅客吞吐量、资产总额和服务半径。
再次,本文对中国空港竞争力的动态形成机理进行了研究。随着中国经济的快速发展,人们对航空运输的需求增长迅速,空港面临着激烈的竞争,所以如何增长空港竞争力成为人们关注的焦点问题。空港竞争力的形成机理非常复杂,现有文献对其研究不足。本文首先从区域发展、生产要素、需求状况和支撑产业四方面构建了空港竞争力的指标体系。利用2006年到2010年25个中国空港的实证数据,通过结构方程和系统动力学研究了空港竞争力的动态形成机理。利用Vensim软件分析了空港竞争力几个重要影响因素的影响机理,也验证了模型的可行性。结果表明,空港自身投资和城市R&D投入是空港竞争力最主要的两个影响因素。本文的结论可以为决策者提供培育空港竞争力方面的参考和指导。
最后,本文对竞争合作环境下的空港动态竞争力评价进行了研究。针对空港动态竞争力具有多维性和复杂性的特点,构建了考虑竞争合作的改进的双链量子遗传算法建立优化BP神经网络的算法,并利用我国2011年和2012年长三角地区的8个空港的实证数据进行研究,得到2013年-2015年8个空港的竞争力表现,弥补了现有研究只评价静态竞争力的不足。结果表明：①改进的双链量子遗传算法建立优化BP神经网络的算法在误差精度、运行时间等方面,它都优于现有的遗传算法优化BP神经网络的算法。②长江三角洲地区的空港差距在缩小,竞争合作呈现愈演愈烈的趋势。
Airport is defined as big-scale civil airport. It is more than an important part of transportation system but is an important part of total national economy system, which has very important influence on the development of national economy. In recent years, Chinese airports have developed greatly with the rapid development of Chinese economy. The total amount of airports begins to take shape, airports'density is becoming bigger, airports' service ability is improving and the degree of modernization is strengthening. However, compared with USA and other developed countries, the total competitiveness of Chinese airports is still limited. The data of passeneger turnover volume in2010is taken as an example. The total passenger turnover volume of China (Excluding HongKong, Macao and Taiwan) ranks second, who is less than USA and is20.8percent of USA; however, the passenger turnover volume of international flights of China ranks tenth, which is18.6percent of USA. Although some factors such as geographical position could result in this situation, China's (Excluding HongKong, Macao and Taiwan) passenger turnover volume of international flights in2010was73488million passenger kilometres. HongKong's passenger turnover volume of international flights in2010was85811million passenger kilometres, which was more than total airports in Chinese Mainland! It can be concluded that the hub situations of Chinese airports must be improved eagerly. It is necessary for Chinese airports to improve airport competitiveness. How to identify the basic influencing factors for Chinese airports? What is the dynamic formation mechanism of airport competitiveness? How to evaluate airports' dynamic competitiveness under competition and cooperation? In view of above questions, this thesis carries out a series of researches.
Firstly, the function of airport competitiveness in the alliance decision-making is analyzed.airport alliance is an important way for airports to reform the resource, but airport' positivity is different due to its own ability, so it is a common concern that how an airport will decide when facing alliance problem. The goal of this paper is to offer alliance decision-making suggestions for airports under the precondition that the alliance game benefits of airports are not same, and the game benefit evaluation index system is built from the aspects of regional development, production factors, demand conditions, support industry and environmental effect. Then the airport network is abstracted as a Small-World network and the airport alliance decision-making problem is studied using Game Dynamics on Small-World Network combining with Entropy Topsis model, then the main factors influencing airport alliance are summarized based on the empirical study of the25big airports in2010. The results show that in the25airports, all international hub airports and big regional hub airports choose non-alliance such as Beijing, Shanghai, Guangzhou, Tianjin and Shenzhen. And when the airports network is stable, their benefit is bigger than other airports. Some regional hub airports, regional main airports and brank airports choose alliance strategy. Although these airports choose alliance strategy, their benefit is lower. It can be. concluded that airports' competitiveness is the basic factor in airport's future development.
Secondly, the basic influencing factors for airport competitiveness are extracted. It is a hot academic topic to identify the influencing factors of airports competitiveness which are numerous and interrelated. This study improves the traditional Decision-making Trial and Evaluation Laboratory (DEMATEL) method according to its limit and proposes BP-DEMATEL method that is suitable for the influencing factors identification. It exploits BP neural network to calculate the weights between object index and influencing factor index and uses the weights to get the direct-relation matrix, then takes advantage of DEMATEL method to study the influencing factors. The empirical analysis of airports competitiveness shows that this method is feasible and can supply theoretical support. The results show that the reason-type factors for airport competitiveness are GDP, Number of graduated students of colleges, investment amount, passenger throughput, traffic consumption of urban residents, service radius and passenger throughput of roadway, railway and waterway. The most influencing factors are passenger throughput, total assets and service radius.
Thirdly, the dynamic formation mechanism of Chinese airport compeititveness is studied. With the rapid development of Chinese economy, the demand of air transportation has increased enormously and airports are facing intensive competition, so the issue of how to enhance airport competitiveness has attracted serious concern of the public. The formation mechanism of airport competitiveness is very complex and the research is insufficient on this topic. In this paper, index system of airport competitiveness is built from four aspects: Regional Development, Production Factors, Demand Conditions and Support Industry. Dynamic formation mechanism of airport competitiveness is studied through Structure Equation Model as well as System Dynamic with the historical data of twenty five Chinese airports from2006to2010. Then the influencing mechanism of some important influencing factors is analyzed with the help of Vensim software, which verifies the rationality of the model. The results show that airport investment and city R&D inputs are the two most important influencing factors of airport competitiveness, which could provide guidance for decision makers on airport competitiveness cultivation.
Fourthly, the dynamic competitiveness of airports is evaluated. Aimed at the multi-dimensional and complex characteristic of airport competitiveness, a new algorithm is proposed in which BP Neural Network is optimized by Improved Double Chains Quantum Genetic Algorithm (IDCQGA). The empirical data of eight airports in Yangtze River Delta in2011and2012is used to verify the feasibility of new algorithm, the competitiveness of eight airports in2013-2015is gotten through the algorithm. The results show:①The new algorithm is better than the existing optimization algorithms in the aspects of error accuracy and run time;②The gaps of the airports in Yangtze River Delta are narrowing, the competition and cooperation are getting stronger and stronger.
引文
[1]Oum T H, Yan J, Yu C Y. Ownership forms matter for airport efficiency:A stochastic frontier investigation of worldwide airports [J]. Journal of Urban Economics, 2008,64 (2):422-435.
[2]Oum T H, Yu C Y. Measuring airports'operating efficiency:a summary of the 2003 ATRS global airport benchmarking report[J]. Transportation Research Part E,2004, 40:515-532.
[3]Park Y. An analysis of the competitive strength of Asian major airports [J]. Journal of Air Transport Management,2003,9 (6):353-360.
[4]Lee H, Yang H M. Strategies for global logistics and economic hub:Incheon International Airport[J]. Journal of Air Transport Management,2003, 9(2):113-121.
[5]Park Y. Application of a fuzzy linguistic approach to analyse Asian airports'competitiveness[J], Transportation Planning and Technology,1997, 20(4):291-309.
[6]Lieshout R, Matsummto H. New international services and the competitiveness of Tokyo International Airport [J]. Journal of Transport Geography,2012,22:53-64.
[7]Peng J L, Zhan C X. A case study on evaluation of airport logistics competitiveness based on AHP[J]. Advanced Materials Research,2012,159:307-312.
[8]张越,胡华清.基于Malmquist生产力指数的我国民用机场运营效率分析[J].系统工程,2006,24(12)：40-45.
[9]姜涛,朱金福.航空公司选择枢纽机场的鲁棒性方法[J].系统工程,2006,24(6)：13-17.
[10]焦朋朋.机场旅客吞吐量的影响机理和预测方法研究[J].交通运输系统工程与信息2005,5(1)：107-110.
[11]应习文,石京.大型枢纽机场可达性量化的初步研究[J].交通运输系统工程与信息,2006,6(6)：136-142.
[12]都业富,朱新华,冯敏.DEA方法在中国民用机场评价中的应用[J].中国民航学院学报,2006,24(6)：46-49.
[13]吴显扬,吴远开.运用AHP方法研究上海浦东空港的亚太竞争力[J].物流技术,2005,9：203-206.
[14]苏道明.机场竞争力评价指标体系的构建[J].中国民航飞行学院学报,2011,22(1)：18-22.
[15]彭语冰,李艳伟.枢纽机场竞争力评价研究[J].技术经济与管理研究,2011,9：11-15.
[16]苏道明,王明英,周官志.机场竞争力形成机理与评价指标体系的构建[J].湖南财经高等专科学校学报,2010,26(123)：98-100.
[17]吴金栋,吕宗平.我国通用航空机场定义及分类方法研究[J].综合运输,2011,4：24-28.
[18]褚衍昌,吴育华.环渤海主要机场竞争力评价研究[J].综合运输,2006,8-9：101-104.
[19]陈林.桂林机场综合竞争力分析与提升研究[D].广西大学硕士学位论文,2012.
[20]程程,李凯莎,刘睿.空港竞争力要素构成与提升措施研究—以广州白云国际机场为例[J].产业与科技论坛,2012,11(2)：24-25.
[21]孙继湖,谭康华.基于民航强国的机场竞争力评价[J].综合运输,2012,12：47-50.
[22]董志毅,彭语冰,崔婷.我国中西部机场特征分析及其竞争力评价研究[J].北京理工大学学报(社会科学版),2007,9(3)：82-87.
[23]王则仪.国际航运中心建设过程中的浦东机场竞争力分析[D].复旦大学硕士论文,2010.
[24]张晓玲,吴春春.基于灰色综合评价法的国内主要机场竞争力评价[J].物流工程与管理,2012,34(11)：166-168.
[25]Fernandes E, Pacheco R R. Airport management:a strategic approach[J]. Transportation,2007,34:129-142.
[26]Zhang A M. Analysis of an international air-cargo hub:the case of Hong Kong [J]. Journal of Air Transport Management,2003,9:123-138.
[27]Sarkis J, Talluri S.,2004. Performance based clustering for benchmarking of US airports[J]. Transportation Research Part A,2004,38:329-346.
[28]Graham A. Airport strategies to gain competitive advantage. GARS:Slots, Airport Competition and Benchmarking of Airports[J]. Bremen,2004,11:19-20.
[29]Pacheco R R, Fernandes E. Managerial efficiency of Brazilian airports[J]. Transportation Research Part A,2003,37:667-680.
[30]Coldren G M, Koppelman F S. Modeling the competition among air-travel itinerary shares:GEV model development[J]. Transportation Research Part A,2005,39: 345-365.
[31]Chen Y, Peng J L. Research on competitiveness evaluation of the airport logistics based on AHP[C].2nd International Conference on Information Science and Engineering,2010,483-486.
[32]Pujinda P. The Competitiveness airport regions in Southeast Asia:the lesson from Two Arch Rivals in Europe [J]. Journal of Environmental Design and Planning,2007, 2:69-80.
[33]Hay S M, Barre J, Bell S N, Perl A. Redesigning an airport for international competitiveness:the politics of administrative innovation at CDG[J]. Journal of Air Transport Management,1998,4(4):189-199.
[34]Inglada V, Rey B, Rodriguez A A, Coto M P. Liberalisation and efficiency in international air transport[J]. Transportation Research Part A,2006,40: 95-105.
[35]Takebayashi M. Evaluation of Asian airports as gateways:application of network equilibrium model[J]. Pacific Economic Review,2011,16(1):64-82.
[36]Barrett S D. Airport competition in the deregulated European aviation market[J]. Journal of Air Transport Management,2000,6,13-27.
[37]Veldhuis J, Essers I. The integrated airport competition model,1998 [J]. Journal of Air Transportation World Wide,1999,4(2):100-130.
[38]McLay P, Reynolds F A. Competition between airport terminals:the issues facing Dublin Airport[J]. Transportation Research Part A,2006,40:181-203.
[39]Pels E, Nijkamp P, Rietveld P. Access to and competition between airports:a case study for the San Francisco Bay area[J]. Transportation Research Part A,2003, 37:71-83.
[40]Martin J C, Roman C. Hub location in the South-Atlantic airline market:a spatial competition game[J]. Transportation Research Part A,2003,37:865-888.
[41]Malighetti P, Paleari S, Redondi R. Connectivity of the European airport network: "Self-help hubbing" and business implication[J]. Journal of Air Transport Management,2008,14(2):53-65.
[42]林华.珠三角机场的联盟之路[J].机场,2004,3：42-44.
[43]张建森.关于港深机场联盟的研究[J].航空运输,2010,5：39-43.
[44]刘正.民航改革催生机场联盟[J].机场,2003,12：53-54.
[45]孙永强.再说长三角机场联盟[J].空运商务,2007,12：7-9.
[46]任新慧.让机场联盟发挥真正的合力作用[N].中国民航报,2012,7,9(05)
[47]隋秀勇.机场联盟蹒跚起步[J].中国产业,2010,7：36-37.
[48]周扬.东北机场联盟同床异梦[N].21世纪经济报道,2010,7,30(19).
[49]韦薇,夏洪山.对整合长三角机场群资源 促进协调发展的思考[J].交通与计算机,2008,26(4)：120-124.
[50]Forsyth P, Niemeier H M, Wolf H. Airport alliances and mergers --Structural change in the airport industry?[J]. Journal of Air Transport Management,2011,17:49-56.
[51]Albers S, Koch B, Ruff C. Strategic alliances between airlines and airports— theoretical assessment and practical evidence[J]. Journal of Air Transport Management,2005,11:49-58.
[52]Pitt M, Werven M V, Price M. Airport facilities management alliances:Problems of competition and complexity[J]. Journal of Retail & Leisure Property,2011, 9(5):391-400.
[53]陈嘉佳.机场联盟动力何在?[N].中国民航报,2012,9,10(5).
[54]Albert R Z. Statistical mechanics of complex networks[D]. Department of Physics Notre Dame 2001.
[55]刘建香.复杂网络及其在国内研究进展的综述[J].系统科学学报,2009,17(4)：31-37.
[56]Watts D J, Strogatz S H.Collective dynamics of'small world'networks[J]. Nature, 1998,393:440-442.
[57]Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science,1999, 286:509-512.
[58]王文旭.复杂网络的演化动力学及网络上的动力学过程研究[D].合肥：中国科学技术大学,2007.
[59]湛文涛,纪庆群.复杂网络上演化博弈动力学研究综述[J].计算机光盘软件与应用,2012,22：75-76.
[60]Szabo G, toke C. Evolutionary prisoner's dilemma game on a square lattice[J]. Physical Review E,1998,58(1):68-69.
[61]杨阳,荣智海,李翔.复杂网络演化博弈理论研究综述[J].复杂系统与复杂性科学,2008,5(4)：47-54.
[62]刘永奎.复杂网络及其网络上的演化动力学研究[D].西安电子科技大学博士学位论文,2010.
[63]崔强,武春友,匡海波.中国空港可持续发展能力评价研究[J].科研管理,2012,33(4)：55-61.
[64]Guimera R, Mossa S, Turtschi A, Amaral L A N. The worldwide air transportation network:Anomalous central ity, community structure, and cities' global roles [J]. PNAS,2005,102 (22):7794-7799.
[65]Li W, Cai X. Statistical analysis of airport network of China[J]. Physical Review E,2004,69 (4):1-6.
[66]Bagler G. Analysis of the airport network of India as a complex weighted network [J]. Physica A,2008,387(12):2972-2980.
[67]Chen X J, Fu F, Wang L. Promoting cooperation by local contribution under stochastic win-stay-lose-shift mechanism[J]. Physica A,2008,387(22):5609-5615.
[68]Chen C T. Extensions of the TOPSIS for group decision-making under fuzzy environment[J]. Fuzzy sets and systems,2000,114(1):1-9.
[69]杨英宝,钟山.中国民航运输机场分类问题的初步探讨[J].机场,2004,11：39-42.
[70]Wu W W. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach [J]. Expert System with Applications,2008,35:828-835.
[71]Kim Y. Study on impact mechanism for beef cattle farming and importance of evaluating agricultural information in Korea using DEMATEL, PCA and AHP [J]. Agricultural Information Research,2006,15(3):267-280.
[72]Shieh J I, Wu H H, Huang K K. A DEMATEL method in identifying key success factors of hospital service quality [J]. Knowledge-Based Systems,2010,23:277-282.
[73]Tseng M L, Lin Y H. Application of fuzzy DEMATEL to develop a cause and effect model of municipal solid waste management in Metro Manila[J]. Environmental monitoring and assessment,2009,158:519-533.
[74]Tseng M L. A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach[J]. Expert System with Application,2009, 36:7738-7748.
[75]Lin C L, Tzeng G H. A value-created system of science (technology) park by using DEMATEL[J]. Expert System with Application,2009,36:9683-9679.
[76]Yang J L, Tzeng G H. An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method[J]. Expert System with Application,2011, 38:1417-1421.
[77]Wu W W, Lee Y T. Developing global managers'competencies using the fuzzy DEMATEL method[J]. Expert System with Application,2007,32:499-507.
[78]Tzeng G H, Chiang C H, Li C W. Evaluating intertwined effects in e-learning programs A novel hybrid MCDM model based on factor analysis and DEMATEL [J]. Expert System with Application,2007,32:1028-1044.
[79]Chang B, Chang C W, Wu C H. Fuzzy DEMATEL method for developing supplier selection criteria[J]. Expert System with Application,2011,38:1850-1858.
[80]Li C W, Tzeng G H. Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall[J]. Expert System with Application, 2009,36:9891-9898.
[81]Tsai W H, Chou W C. Selecting management systems for sustainable development in SMEs:A novel hybrid model based on DEMATEL, ANP, and ZOGP[J]. Expert System with Application,2009,36:1444-1458.
[82]Chen J K, Chen I S. Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education [J]. Expert System with Application,2010,37:1981-1990.
[83]Tseng M L. Using the extension of DEMATEL to integrate hotel service quality perceptions into a cause-effect model in uncertainty[J]. Expert System with Application,2009,36:9015-9023.
[84]吴微.神经网络计算[M].北京：高等教育出版社,2007.
[85]飞思科技产品研发中心.神经网络理论与MATLAB 7实现[M].北京：电子工业出版社,2005.
[86]王军.产业组织演化理论及实证研究[D].山东大学博士论文,2006.
[87]王洁.产业聚集理论与应用的研究[D].同济大学博士论文,2007.
[88]何苗.基于分形理论的农业产业集群形成机理研究[D].河南大学硕士论文,2009.
[89]斯蒂芬.格瑞理.顶级竞争力[M].北京：东方出版社,2008.
[90]倪鹏飞等.2010年中国城市竞争力蓝皮书：中国城市竞争力报告[M].北京：社科文献出版社,2010.
[91]刘炳胜,王雪青,曹琳剑.基于SEM和SD组合的中国建筑产业竞争力动态形成机理仿真[J].系统工程理论与实践,2010(11)：2063--2070.
[92]刘炳胜,王雪青,李冰.中国建筑产业竞争力形成机理分析-基于PLS结构方程模型的实证研究[J].数理统计与管理,2011,30(1)：12-22.
[93]赵璟,党兴华.系统动力学模型在城市群发展规划中的应用[J].系统管理学报,2008(4)：395-408.
[94]Cui Q, Kuang H B, Wu C Y, Li Y. Dynamic formation mechanism of airport competitiveness:the case of China[J]. Transportation Research Part A:Policy and Practice,2013,47(1):10-18.
[95]杨黎萌,刘开第.BP神经网络在房地产估价中的应用[J].河北建筑科技学院学报,2004,21(2)：105-109.
[96]傅毓维,尹航,杨贵彬.BP神经网络在企业经营绩效评价中的应用[J].运筹与管理,2006,15(4)：137-140.
[97]郭阳.PS0_BP神经网络在商业银行信用风险评估中的应用研究[D].厦门大学硕士论文,2009.
[98]史成东,陈菊红,郭福利.粗糙集和BP神经网络在供应链绩效评价中的应用研究[J].软科学,2008,‘22(3)：9-13.
[99]汪克亮,杨力,查甫更.改进BP神经网络在企业网络营销绩效评价中的应用[J].商业研究,2008,3：64-68.
[100]邓宝,宋瑞.基于BP神经网络的安全评价方法研究[J].安全与环境工程,2005,12(2)：61-64.
[101]赵冰.基于BP神经网络的港口竞争力评价研究[J].大连海事大学硕士论文,2008.
[102]李明月,赖笑娟.基于BP神经网络方法的城市土地生态安全评价[J].经济地理,2011,31(2)：289-293.
[103]戴丹.BP神经网络用于市场预测的研究[D].武汉理工大学硕士论文,2006.
[104]李旭军.BP神经网络在经济预测中的应用[D].华中师范大学硕士论文,2008.
[105]李霞.基于BP神经网络的销售预测研究[D].上海交通大学硕士论文,2012.
[106]雷可为,陈瑛.基于BP神经网络和ARIMA组合模型的中国入境游客量预测[J].旅游学刊,2007,4：20-25.
[107]褚诚山,张宏伟,郭军.基于遗传算法和BP神经网络的用水量预测[J].中国农村水利水电,2006,4：36-38.
[108]杨淑娥,黄礼.基于BP神经网络的上市公司财务预警模型[J].系统工程理论与实践,2005,1：12-17.
[109]金成晓,俞婷婷.基于BP神经网络的我国制造业产业安全预警研究[J].北京工业大学学报,2010,10(1)：8-16.
[110]范秋芳.基于BP神经网络的中国石油安全预警研究[J].运筹与管理,2007,16(5)：100-105.
[111]张兰霞,王俊,张燕,吴小康.基于BP神经网络的人力资源管理风险预警模型[J].南开管理评论,2007,10(6)：78-85.
[112]朱彩荣,倪宗瓒.BP神经网络的科研基金效果影响因素[J].现代预防医学,2002,29(2)：132-134.
[113]姜林奎,付存军,曹玉昆.基于BP方法的企业核心竞争力关键因素探析[J].哈尔滨工程大学学报,2009,30(10)：1188-1193.
[114]赵向红,李沛,李朝峰.基于BP神经网络的居民生活质量影响因素分析[J].江南大学学报(自然科学版),2012,11(6)：642-646.
[115]李英龙,Gama C D D.基于BP网络的矿业因素分析方法的再研究[C].第六届全国采矿学术会议文集,1999,642-644.
[116]刘志刚,许少华.基于混合量子遗传算法的过程神经元网络训练[J].计算机应用研究,2009,26(8)：2888-2890.
[117]Narayanan A, Moore M. Quantum-in spired genetic algorithm [C]. Proceedings of IEEE International Conference on Evolutionary Computation, Nagoya, Japan,1996, 61-66.
[118]Han K H, Kim J H. Genetic quantum alrorithm and its application to combinational optimization problem[C]. Proceedings of the International Congress on Evolutionary Computation. IEEE Press,2000,1354-1360.
[119]Han K H, Kim J H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization[J]. IEEE Trans Evolutionary Computation,2002, 6(6):580-593.
[120]潘振东,唐加福,韩毅.带货物权重的车辆路径问题及遗传算法[J].管理科学学报,2007,10(3)：23-29.
[121]陈南祥,李跃鹏,徐晨光.基于多目标遗传算法的水资源优化配置[J].水利学报,2006,37(3)：308-313.
[122]丁书斌.基于混合遗传算法的车问调度方法研究与应用[D].大连理工大学硕士论文,2006.
[123]冯红娟.基于遗传算法的车间作业调度问题研究[D].长春理工大学硕士论文,2008.
[124]王少波,解建仓,孔珂.自适应遗传算法在水库优化调度中的应用[J].水利学报,2006,37(4)：480-485.
[125]郎茂祥,胡思继.用混合遗传算法求解物流配送路径优化问题的研究[J].中国管理科学,2002,10(5)：51-56.
[126]王成宝,任传祥,尹唱唱,陈梦.基于遗传算法的BP神经网络短时交通流预测[J].山东交通科技,2012,5：5-12.
[127]叶枫,孙科达.基于遗传算法的BP神经网络模型在道路交通事故宏观预测中的应用[J].浙江工业大学学报,2008,36(3)：338-342.
[128]迟宝明,林岚,丁元芳.基于遗传算法的BP神经网络模型在地下水动态预测中的应用研究[J].工程勘察,2008,9：36-41.
[129]吴建生,金龙,农吉夫.遗传算法BP神经网络的预报研究和应用[J].数学的实践与认识,2005,35(1)：83-88.
[130]李士勇、李盼池.量子计算与量子优化算法[M].哈尔滨工业大学出版社,2009.
[131]Li P C, Li S Y. Quantum-inspired evolutionary algorithm for contimous spaces optimization[J]. Chinese Journal of Electronics,2008,17(1):80-84.
[132]李士勇,李盼池.基于实数编码和目标函数梯度的量子遗传算法[J].哈尔滨工业大学学报,2006,38(8)：1216-1218.
[133]Feynman R P. Simulating physics with computers [J]. International Journal of Theoretical Physics,1982,21(6/7):467-488.
[134]乔双,董智红.BP网络初始权值的选取方法[J].东北师大学报(自然科学版),2004,36(3)：25-30.
[135]闽泉,叶水生,郭荣传,石海霞.利用量子遗传算法改进BP学习算法[J].计算机系统应用,2009,5：53-55.
[136]曹茂俊,尚福华.基于双链量子遗传算法的过程神经元网络训练[J].计算机测量与控制,2010,18(2)：407-410.