基于实时气象因素的短期负荷预测方法研究
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摘要
电力系统短期负荷预测是电力市场及其技术支持系统的重要组成部分,是电力市场环境下安排调度计划、交易计划的前提和基础,其预测精度关系到整个电力系统的效率、效益和安全性,因此,如何提高负荷预测的精度一直是研究人员关心的热点和难点问题。
研究表明,负荷受经济、用电结构、电价、气象等诸多因素的影响。近年来,随着经济的发展和人们生活水平的提高,居民生活用电迅速增长,降温取暖负荷所占比重逐渐增大,且这部分负荷对于气象变化极为敏感,往往天气状况一旦变化,负荷水平随即跟着显著变动,气象因素与负荷的关系问题已经越来越多的引起人们的关注。
本文首先对A地区的负荷特性和气候特点进行了分析,提出了适合该地区的负荷分解模型,并将负荷分解成了基础负荷分量、气象敏感负荷分量和随机负荷分量三部分,其中气象敏感分量极大地受到天气因素的影响。
接着本文对总负荷、气象敏感负荷与气象因素的关系进行了研究,研究从日特征气象因素和实时气象因素两个角度进行,并从中提取出了对负荷变化影响最大的日平均温度、日最大湿度和风速等日特征气象因素及实时温度、实时湿度等实时气象因素;针对该地区夏季持续高温的特点,温度对负荷具有一定的累积效应,文中对此也进行了简要的分析;另外,本文提出了依据负荷特性和气象条件来综合选取相似日的方法,该方法能有效避免传统选择方法不够灵活的缺点,在对预测日气象复杂条件下的相似日选取更具方便性。
在以上研究的基础上,本文还提出了基于负荷分解和实时气象因素的短期负荷预测方法。借鉴中长期负荷中的灰色系统GM(1,1)模型对基础负荷分量进行预测,对气象敏感负荷分量采用基于Levernberg-Marquardt优化算法的BP神经网络模型进行预测,并通过该地区2007年夏季一个月的负荷预测仿真,证明了本文所提出方法,能够显著提高预测的速度和精度。
Short-term load forecasting is one of the important parts of power market and its technical support system,it is the presupposition and basis of arranging dispatch plan and deal plan under power market circumstances. The forecast accuracy relates to the efficiency、benefit and security of the entire power system, therefore, how to raise the accuracy of load forecasting has been a hotspot also difficult issue for research workers.
Research shows that power load is influenced by many factors such as economy、power-utilization construction、power price、weather, etc. Recently, civilian power consumption grows quickly along with economic growth and people’s living standard upturn, the proportion of heating and cooling load in total load increases rapidly. Moreover, it is very sensitive to weather changes,load level vary evidently in case weather conditions changes. The relationship between weather factors and load has raised more and more concerns.
In this thesis, firstly, the load and weather characteristics of area A are analyzed, and then a load decomposing model which is suitable to the area is built, the total load is decomposed into base load、weather sensitive load and random load, among which the weather sensitive load is influenced by weather factors greatly.
Secondly, the relationship between total load、weather sensitive load and weather factors are studied, the study begins from daily characteristic and hourly weather factors, then daily characteristic weather factors such as daily average temperature、daily maximum humidity and hourly weather factors such as hourly temperature、hourly humidity are selected; As it is always a continuous hot weather for this area in summer, so temperature has an accumulation effect on load, in this thesis, the accumulative effect is also analyzed briefly; In addition, a method for similar days selecting based on load characteristics and weather conditions is proposed, it can avoid the inflexibility of traditional method, and has advantage over that under complex weather conditions of the forecasting day.
Finally, on the basis of the studies above, a short-term load forecasting method based on load decomposing and hourly weather factors is proposed in this paper. The base load component is forecasted by gray system GM(1,1) model which is usually used in medium and long term load forecasting, the weather sensitive load is forecasted by a BP neural network which is optimized by Levernberg-Marquardt algorithm. The availability of the method is proved by forecasting simulation of this area in summer 2007, it can improve the forecasting speed and accuracy remarkably.
研究表明,负荷受经济、用电结构、电价、气象等诸多因素的影响。近年来,随着经济的发展和人们生活水平的提高,居民生活用电迅速增长,降温取暖负荷所占比重逐渐增大,且这部分负荷对于气象变化极为敏感,往往天气状况一旦变化,负荷水平随即跟着显著变动,气象因素与负荷的关系问题已经越来越多的引起人们的关注。
本文首先对A地区的负荷特性和气候特点进行了分析,提出了适合该地区的负荷分解模型,并将负荷分解成了基础负荷分量、气象敏感负荷分量和随机负荷分量三部分,其中气象敏感分量极大地受到天气因素的影响。
接着本文对总负荷、气象敏感负荷与气象因素的关系进行了研究,研究从日特征气象因素和实时气象因素两个角度进行,并从中提取出了对负荷变化影响最大的日平均温度、日最大湿度和风速等日特征气象因素及实时温度、实时湿度等实时气象因素;针对该地区夏季持续高温的特点,温度对负荷具有一定的累积效应,文中对此也进行了简要的分析;另外,本文提出了依据负荷特性和气象条件来综合选取相似日的方法,该方法能有效避免传统选择方法不够灵活的缺点,在对预测日气象复杂条件下的相似日选取更具方便性。
在以上研究的基础上,本文还提出了基于负荷分解和实时气象因素的短期负荷预测方法。借鉴中长期负荷中的灰色系统GM(1,1)模型对基础负荷分量进行预测,对气象敏感负荷分量采用基于Levernberg-Marquardt优化算法的BP神经网络模型进行预测,并通过该地区2007年夏季一个月的负荷预测仿真,证明了本文所提出方法,能够显著提高预测的速度和精度。
Short-term load forecasting is one of the important parts of power market and its technical support system,it is the presupposition and basis of arranging dispatch plan and deal plan under power market circumstances. The forecast accuracy relates to the efficiency、benefit and security of the entire power system, therefore, how to raise the accuracy of load forecasting has been a hotspot also difficult issue for research workers.
Research shows that power load is influenced by many factors such as economy、power-utilization construction、power price、weather, etc. Recently, civilian power consumption grows quickly along with economic growth and people’s living standard upturn, the proportion of heating and cooling load in total load increases rapidly. Moreover, it is very sensitive to weather changes,load level vary evidently in case weather conditions changes. The relationship between weather factors and load has raised more and more concerns.
In this thesis, firstly, the load and weather characteristics of area A are analyzed, and then a load decomposing model which is suitable to the area is built, the total load is decomposed into base load、weather sensitive load and random load, among which the weather sensitive load is influenced by weather factors greatly.
Secondly, the relationship between total load、weather sensitive load and weather factors are studied, the study begins from daily characteristic and hourly weather factors, then daily characteristic weather factors such as daily average temperature、daily maximum humidity and hourly weather factors such as hourly temperature、hourly humidity are selected; As it is always a continuous hot weather for this area in summer, so temperature has an accumulation effect on load, in this thesis, the accumulative effect is also analyzed briefly; In addition, a method for similar days selecting based on load characteristics and weather conditions is proposed, it can avoid the inflexibility of traditional method, and has advantage over that under complex weather conditions of the forecasting day.
Finally, on the basis of the studies above, a short-term load forecasting method based on load decomposing and hourly weather factors is proposed in this paper. The base load component is forecasted by gray system GM(1,1) model which is usually used in medium and long term load forecasting, the weather sensitive load is forecasted by a BP neural network which is optimized by Levernberg-Marquardt algorithm. The availability of the method is proved by forecasting simulation of this area in summer 2007, it can improve the forecasting speed and accuracy remarkably.
引文
[1]罗滇生,姚建刚,何洪英,等.基于自适应滚动优化的电力负荷多模型组合预测系统的研究与开发.中国电机工程学报,2003,23(5):58-61
[2]汪峰,于尔铿,阎承山,等.基于因素影响的电力系统短期负荷预测方法的研究.中国电机工程学报,1999,19(8):54-58
[3] Noureddine, A.H. Alouani, A.T. Chandrasekaran, A. A new technique for short-term residential electric load forecasting including weather and lifestyle influences. Circuits and Systems, 1992,10(2):1419 - 1427
[4]贺蓉,曾刚,姚建刚,等.天气敏感神经网络在地区电网短期负荷预测中的应用.电力系统自动化,2001,25(17):32-35
[5]杨正瓴,田勇,林孔元.短期负荷预测“双周期加混沌”法中的多步法与气象因子的使用.电网技术,2004,28(12):20-24
[6]李扬,王治华,卢毅,等.南京市夏季气温-日峰荷特性分析.电网技术,2001,25(7): 63-67
[7]谢宏,陈志业,牛东晓,等.基于小波分解与气象因素影响的电力系统日负荷预测模型研究.中国电机工程学报,2001,21(5):5-10
[8]朱陶业,李应求,张颖,等.提高时间序列气象适应性的短期电力负荷预测算法.中国电机工程学报,2006,26(23):14-19
[9]朱晟,蒋传文,侯志俭.基于气象负荷因子的Elman神经网络短期负荷预测.电力系统及其自动化学报,2005,17(1):23-26
[10]高霞,曾新,马骋.决策树技术分析气象因子对电力负荷预测的影响.气象,2008, 34(3):106-111
[11]张国庆,韩新阳,王辉,等.气象因素对负荷和电量影响的研究.中国电机工程学报,2001,21(8):253-255
[12] Ching-Lai Hor, Simon J.Watson,Shanti Majithia. Analying the impact of weather variables on monthly electricity demand. IEEE Trans. Power Syst, 2005,20(4): 2078-2085
[13]刘运红,姜铁兵,陈丰华,等.基于气象因素的短期电力负荷ANN预报模型.水电能源科学,2001,19(4):51-55
[14]胡江林,陈正洪,洪斌,等.基于气象因子的华中电网负荷预测方法研究.应用气象学报,2002,13(5):600-608
[15]康重庆,夏清,刘梅.电力系统负荷预测.北京:中国电力出版社,2007:185-187
[16]龙立波,基于负荷特性分析的短期负荷预测的研究与实现:[湖南大学硕士学位论文].长沙:湖南大学,2007:1-1
[17]姚建刚,章建.电力市场分析.北京:高等教育出版社,1999
[18]王霄,刘莉,吴志宏.灰色GM(1,1)模型在电力系统负荷预测中的应用.沈阳工程学院学报(自然科学版),2005,1(2):66-67
[19]倪军,杨期余.专家系统在电力系统负荷预测中的应用研究.湖南电力技术, 1994,6(4):14-17
[20]孙云.小波分析在电力系统短期负荷预测中的应用.[浙江大学硕士学位论文].杭州:浙江大学,2005,43-60
[21]李天云,刘自发.电力系统负荷的混沌特性及预测.中国电机工程学报, 2000,20(11):36-40
[22]田景文,高美娟.人工神经网络算法研究及应用.北京:北京理工大学出版社, 2006,24-39
[23]陈祥光,裴旭东.人工神经网络技术及应用.北京:中国电力出版社,2003,6-8
[24]杨建刚.人工神经网络实用教程.杭州:浙江大学出版社,2001
[25] X Wang, N Hatziargyriou and L H Tsoukalas. A New Methodology for Nodal Load Forecasting in Deregulated Power System. IEEE Power Engineering Review, 2002,22(5):48-51
[26] T Takara, H Uezato and T Funabashi. One Hour Ahead Load Forecasting Using Neural Network. IEEE Transactions on Power Systems, 2002,17(1):113-118
[27] W Charytoniuk and M S Chen. Very Short Term Load Forecasting Using Artificial Neural Network. IEEE Trans. Power Syst, 2000, 15(1): 263-268
[28]周佃民,管晓宏,孙婕,等.基于神经网络的电力系统短期负荷预测研究.电网技术,2002,26(2):10-13
[29] J.Taylor and R.Buizza,Neural network load forecasting with weather ensemble predictions. IEEE Trans. Power Syst, 2002,17(3):626-632
[30]张亚军,刘志刚,张大波.一种基于多神经网络的组合负荷预测模型.电网技术, 2006,30(21):21-25
[31]马文晓,白晓民,沐连顺.基于人工神经网络和模糊推理的短期负荷预测方法.电网技术,2002,26(3):49-51
[32]徐光虎,申刚,顾洁,等.基于自适应进化规划的电力系统负荷预测综合模型.电力自动化设备,2002,22(6):29-32
[33]康重庆,夏清,沈瑜,等.电力系统负荷预报的综合模型.清华大学学报(自然科学版),1999,39(1):8-11
[34]安德洪,韩文秀,岳毅宏.组合预测法的改进及其在负荷预测中的应用.系统工程与电子技术,2004,26(6):842-844
[35]周显玉.短期电力负荷预测的基本模型.科技创新导报,2008,9:51-52
[36]邱向京,周峰,刘升,等.福建省年最大降温负荷测算分析与研究.华东电力, 2005,33(10):86-90
[37]张凯,姚建刚,李伟,等.基于功率谱分解和实时气象因素的短期负荷预测.电网技术,2007,31(23):47-51
[38] Hanjie Chen, Yijun Du, John N.Jiang. Weather Sensitive Short-Term Load Forecasting Using Knowledge-Based ARX Models. IEEE Transactions on Power Systems,2005,vol 1,pp 190-196.
[39] E.G.Contaxi, S.Kavatza. Application of a Weather-Sensitive Peak Load Forecasting Model to the Hellenic System. IEEE MELECON,2004,3(3):819-822
[40] Chen.S.T, Yu,D.C, Monghaddamjo.A.R. Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE, 1992,7(3): 1098-1105
[41] Campo R,Ruiz P. Adaptive Weather-Sensitive Short Term Load Forecast. IEEE. Transactions on Power System,1987, 2(3):592-598
[42] Ruzic, S. Vuckovic, A. Nikolic, N. Weather sensitive method for short term load forecasting in Electric Power Utility of Serbia. Power Systems, IEEE Transactions on, 2003,18(4): 1581-1586
[43]姚莉.数字序列相关性分析方法的讨论.数理医药学杂志.2003,16(2):106-108
[44]王增珍,李君荣.关联度分析及其与相关分析的比较.中国卫生统计,1991,8(6): 22-25
[45]广西园艺编辑部.积温及其计算方法.广西园艺,2004, 15(3): 53-55
[46]吴兑.多种人体舒适度预报公式讨论.气象科技,2003,(31)6:370-372
[47]李源,袁业畅,陈云生.武汉市人体舒适度计算方法及其预报.湖北气象, 2000,(1):27-28
[48]袁飞,肖晶,卢毅.基于人体舒适度指数的夏季负荷特性分析.江苏电机工程, 2005,24(6):5-7
[49]刘梅,于波.人体舒适度研究现状及其开发应用前景.气象科技,2002,30(1): 11-14
[50]王远飞,沈愈.上海市夏季温湿效应与人体舒适度.华东师范大学学报(自然科学版),1998,3:60-66
[51]李晓梅,周晖,李冬梅.夏季日峰荷与有效温度的灰色建模及灵敏度分析.电网技术,2004,28(14):23-27
[52]罗滇生,何洪瑛.基于形态相似准则的曲线拟合算法及其在超短期负荷预测中的应用.电网技术,2007,31(21):81-84
[53]张润楚.多元统计分析.北京:科学出版社,2006,242-243
[54]阎惠芳,李杜宗,黄跃青,等.常用相似性判据的检验和综合相似系数的使用.气象科技,2003,31(4):211-215
[55]周美立,何鹏.基于特征差异的系统相似性度量方法.机械设计,2005,22(增刊):71-72
[56]赖桃桃,冯少荣.聚类算法中的相似性度量方法研究.心智与计算,2008, 2(2):176-181
[57]邓聚龙.灰色预测与决策.武汉:华中理工大学出版社,1986,97-148
[58] K.Levenberg. A method for the solution of certain problems in least squares. Quare.Appl.Math,1944,vol 2,pp 164-168
[59] Marquardt D.W. An algorithm for least squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 1963,(11):431-441
[60] Hagan M T, Menhaj M B. Training feedforward networks with the marquardt algorithm. IEEE Trans Neural Net,1994,5(6):989-993
[61] Lera G, Pinzolas M. Neighborhood based Levenberg Marquardt algorithm for neural network training. IEEE Transaction on Neural Network, 2003,13(5): 1200-1203
[62]陈万海,王婷,苗廷辉.基于LMBP神经网络的非线性畸变图像校正研究.哈尔滨工程大学学报,2006,27(增刊):173-176
[63]赵宏,周瑞祥,林廷坅.基于Levenberg-Marquardt算法的神经网络监督控制.西安交通大学学报,2002,36(5):523-527
[64]苏伟,卢玉蓉.基于LMBP算法的Drazin矩阵逆的求解.成都理工大学学报(自然科学版),2005,32(4):217-220
[65]王赟松,许洪国.快速收敛的BP神经网络算法.吉林大学学报(工学版), 2003,33(4):79-84
[66]梁宇,尤志锋,王海峰,等.基于LMBP算法的防空兵群射击指挥能力评估模型.火力与指挥控制,2005,30(增刊):31-36
[67]杨奎河,王宝树,赵玲玲.神经网络短期负荷预测的输入变量选择研究.电子测量与仪器学报,2004,18(3):13-17
[2]汪峰,于尔铿,阎承山,等.基于因素影响的电力系统短期负荷预测方法的研究.中国电机工程学报,1999,19(8):54-58
[3] Noureddine, A.H. Alouani, A.T. Chandrasekaran, A. A new technique for short-term residential electric load forecasting including weather and lifestyle influences. Circuits and Systems, 1992,10(2):1419 - 1427
[4]贺蓉,曾刚,姚建刚,等.天气敏感神经网络在地区电网短期负荷预测中的应用.电力系统自动化,2001,25(17):32-35
[5]杨正瓴,田勇,林孔元.短期负荷预测“双周期加混沌”法中的多步法与气象因子的使用.电网技术,2004,28(12):20-24
[6]李扬,王治华,卢毅,等.南京市夏季气温-日峰荷特性分析.电网技术,2001,25(7): 63-67
[7]谢宏,陈志业,牛东晓,等.基于小波分解与气象因素影响的电力系统日负荷预测模型研究.中国电机工程学报,2001,21(5):5-10
[8]朱陶业,李应求,张颖,等.提高时间序列气象适应性的短期电力负荷预测算法.中国电机工程学报,2006,26(23):14-19
[9]朱晟,蒋传文,侯志俭.基于气象负荷因子的Elman神经网络短期负荷预测.电力系统及其自动化学报,2005,17(1):23-26
[10]高霞,曾新,马骋.决策树技术分析气象因子对电力负荷预测的影响.气象,2008, 34(3):106-111
[11]张国庆,韩新阳,王辉,等.气象因素对负荷和电量影响的研究.中国电机工程学报,2001,21(8):253-255
[12] Ching-Lai Hor, Simon J.Watson,Shanti Majithia. Analying the impact of weather variables on monthly electricity demand. IEEE Trans. Power Syst, 2005,20(4): 2078-2085
[13]刘运红,姜铁兵,陈丰华,等.基于气象因素的短期电力负荷ANN预报模型.水电能源科学,2001,19(4):51-55
[14]胡江林,陈正洪,洪斌,等.基于气象因子的华中电网负荷预测方法研究.应用气象学报,2002,13(5):600-608
[15]康重庆,夏清,刘梅.电力系统负荷预测.北京:中国电力出版社,2007:185-187
[16]龙立波,基于负荷特性分析的短期负荷预测的研究与实现:[湖南大学硕士学位论文].长沙:湖南大学,2007:1-1
[17]姚建刚,章建.电力市场分析.北京:高等教育出版社,1999
[18]王霄,刘莉,吴志宏.灰色GM(1,1)模型在电力系统负荷预测中的应用.沈阳工程学院学报(自然科学版),2005,1(2):66-67
[19]倪军,杨期余.专家系统在电力系统负荷预测中的应用研究.湖南电力技术, 1994,6(4):14-17
[20]孙云.小波分析在电力系统短期负荷预测中的应用.[浙江大学硕士学位论文].杭州:浙江大学,2005,43-60
[21]李天云,刘自发.电力系统负荷的混沌特性及预测.中国电机工程学报, 2000,20(11):36-40
[22]田景文,高美娟.人工神经网络算法研究及应用.北京:北京理工大学出版社, 2006,24-39
[23]陈祥光,裴旭东.人工神经网络技术及应用.北京:中国电力出版社,2003,6-8
[24]杨建刚.人工神经网络实用教程.杭州:浙江大学出版社,2001
[25] X Wang, N Hatziargyriou and L H Tsoukalas. A New Methodology for Nodal Load Forecasting in Deregulated Power System. IEEE Power Engineering Review, 2002,22(5):48-51
[26] T Takara, H Uezato and T Funabashi. One Hour Ahead Load Forecasting Using Neural Network. IEEE Transactions on Power Systems, 2002,17(1):113-118
[27] W Charytoniuk and M S Chen. Very Short Term Load Forecasting Using Artificial Neural Network. IEEE Trans. Power Syst, 2000, 15(1): 263-268
[28]周佃民,管晓宏,孙婕,等.基于神经网络的电力系统短期负荷预测研究.电网技术,2002,26(2):10-13
[29] J.Taylor and R.Buizza,Neural network load forecasting with weather ensemble predictions. IEEE Trans. Power Syst, 2002,17(3):626-632
[30]张亚军,刘志刚,张大波.一种基于多神经网络的组合负荷预测模型.电网技术, 2006,30(21):21-25
[31]马文晓,白晓民,沐连顺.基于人工神经网络和模糊推理的短期负荷预测方法.电网技术,2002,26(3):49-51
[32]徐光虎,申刚,顾洁,等.基于自适应进化规划的电力系统负荷预测综合模型.电力自动化设备,2002,22(6):29-32
[33]康重庆,夏清,沈瑜,等.电力系统负荷预报的综合模型.清华大学学报(自然科学版),1999,39(1):8-11
[34]安德洪,韩文秀,岳毅宏.组合预测法的改进及其在负荷预测中的应用.系统工程与电子技术,2004,26(6):842-844
[35]周显玉.短期电力负荷预测的基本模型.科技创新导报,2008,9:51-52
[36]邱向京,周峰,刘升,等.福建省年最大降温负荷测算分析与研究.华东电力, 2005,33(10):86-90
[37]张凯,姚建刚,李伟,等.基于功率谱分解和实时气象因素的短期负荷预测.电网技术,2007,31(23):47-51
[38] Hanjie Chen, Yijun Du, John N.Jiang. Weather Sensitive Short-Term Load Forecasting Using Knowledge-Based ARX Models. IEEE Transactions on Power Systems,2005,vol 1,pp 190-196.
[39] E.G.Contaxi, S.Kavatza. Application of a Weather-Sensitive Peak Load Forecasting Model to the Hellenic System. IEEE MELECON,2004,3(3):819-822
[40] Chen.S.T, Yu,D.C, Monghaddamjo.A.R. Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE, 1992,7(3): 1098-1105
[41] Campo R,Ruiz P. Adaptive Weather-Sensitive Short Term Load Forecast. IEEE. Transactions on Power System,1987, 2(3):592-598
[42] Ruzic, S. Vuckovic, A. Nikolic, N. Weather sensitive method for short term load forecasting in Electric Power Utility of Serbia. Power Systems, IEEE Transactions on, 2003,18(4): 1581-1586
[43]姚莉.数字序列相关性分析方法的讨论.数理医药学杂志.2003,16(2):106-108
[44]王增珍,李君荣.关联度分析及其与相关分析的比较.中国卫生统计,1991,8(6): 22-25
[45]广西园艺编辑部.积温及其计算方法.广西园艺,2004, 15(3): 53-55
[46]吴兑.多种人体舒适度预报公式讨论.气象科技,2003,(31)6:370-372
[47]李源,袁业畅,陈云生.武汉市人体舒适度计算方法及其预报.湖北气象, 2000,(1):27-28
[48]袁飞,肖晶,卢毅.基于人体舒适度指数的夏季负荷特性分析.江苏电机工程, 2005,24(6):5-7
[49]刘梅,于波.人体舒适度研究现状及其开发应用前景.气象科技,2002,30(1): 11-14
[50]王远飞,沈愈.上海市夏季温湿效应与人体舒适度.华东师范大学学报(自然科学版),1998,3:60-66
[51]李晓梅,周晖,李冬梅.夏季日峰荷与有效温度的灰色建模及灵敏度分析.电网技术,2004,28(14):23-27
[52]罗滇生,何洪瑛.基于形态相似准则的曲线拟合算法及其在超短期负荷预测中的应用.电网技术,2007,31(21):81-84
[53]张润楚.多元统计分析.北京:科学出版社,2006,242-243
[54]阎惠芳,李杜宗,黄跃青,等.常用相似性判据的检验和综合相似系数的使用.气象科技,2003,31(4):211-215
[55]周美立,何鹏.基于特征差异的系统相似性度量方法.机械设计,2005,22(增刊):71-72
[56]赖桃桃,冯少荣.聚类算法中的相似性度量方法研究.心智与计算,2008, 2(2):176-181
[57]邓聚龙.灰色预测与决策.武汉:华中理工大学出版社,1986,97-148
[58] K.Levenberg. A method for the solution of certain problems in least squares. Quare.Appl.Math,1944,vol 2,pp 164-168
[59] Marquardt D.W. An algorithm for least squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 1963,(11):431-441
[60] Hagan M T, Menhaj M B. Training feedforward networks with the marquardt algorithm. IEEE Trans Neural Net,1994,5(6):989-993
[61] Lera G, Pinzolas M. Neighborhood based Levenberg Marquardt algorithm for neural network training. IEEE Transaction on Neural Network, 2003,13(5): 1200-1203
[62]陈万海,王婷,苗廷辉.基于LMBP神经网络的非线性畸变图像校正研究.哈尔滨工程大学学报,2006,27(增刊):173-176
[63]赵宏,周瑞祥,林廷坅.基于Levenberg-Marquardt算法的神经网络监督控制.西安交通大学学报,2002,36(5):523-527
[64]苏伟,卢玉蓉.基于LMBP算法的Drazin矩阵逆的求解.成都理工大学学报(自然科学版),2005,32(4):217-220
[65]王赟松,许洪国.快速收敛的BP神经网络算法.吉林大学学报(工学版), 2003,33(4):79-84
[66]梁宇,尤志锋,王海峰,等.基于LMBP算法的防空兵群射击指挥能力评估模型.火力与指挥控制,2005,30(增刊):31-36
[67]杨奎河,王宝树,赵玲玲.神经网络短期负荷预测的输入变量选择研究.电子测量与仪器学报,2004,18(3):13-17
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