粗糙集属性约简和聚类算法及其在电力自动化中的应用研究
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摘要
数据挖掘(Data Mining,DM)是一种新兴的数据分析方法,它可以帮助人们充分应用数据中所蕴涵的信息,成为人工智能研究中非常活跃的领域。粗糙集是一种处理模糊和不确定知识的理论,聚类分析在没有先验知识时发现数据的规律,为人们提供了新的数据分类的方法。虽然在粗糙集和聚类分析方面有很多理论和方法产生,但由于数据对象的千变万化,需要我们不断对这些技术进行完善,以满足应用的需要。
在研究粗糙集关联影响度的基本概念和性质的基础上,通过分析属性之间的相互影响,建立属性的关联影响度矩阵,以属性的关联影响度作为启发条件,有效地删除冗余属性,获得能反映出属性之间相互影响的约简集。实验表明,基于关联影响度的属性约简算法可以得到关联影响度较大的属性组成的约简集。这种概念拓宽了粗糙集的应用范围,为数据挖掘提供了新的方法。
在粗糙集关联影响概念的基础上,对基于关联影响属性动态约简的概念和算法进行了研究,通过计算粗糙集中样本的激活状态ρ(U)和睡眠状态σ(U)对属性约简集的影响,在ρ(U)→σ(U)时,从约简集中将冗余的属性删除,在σ(U)→ρ(U)时,将必要的属性增加到约简集,这些算法和概念是有利于描述事物状态转换的方法。
智能监控系统是工业自动化控制的核心,粗糙集理论为它提供了切实可行的实时决策规则,基于实时性的属性约简算法将弱实时性属性删除,保留强实时性约简集,以保证决策系统的实时性指标,该算法使粗糙集在实时决策系统的应用更加广泛。
属性分类的约简算法能满足决策表中条件属性的分类要求,该算法按照分类函数对条件属性进行分类计算后,将次要的属性子集删除,求得属性分类约简集。实验表明,该算法能够在原决策能力不变的情况下,有效地删除部分属性,解决了属性分类的问题。
将所研究的属性约简算法应用到配电网故障诊断和电网连锁故障诊断预警系统中。在分析和研究配电网故障诊断系统的属性选择和规则产生方法的基础上,将实时性属性约简、属性分类约简算法应用到配电网故障诊断系统中。通过电工理论计算电网连锁故障诊断预警系统的属性值,求出负荷转移情况下属性之间相互产生的关联影响以及线路的故障度。通过关联影响属性约简算法的应用,观察属性的变化,达到预测故障、及时排除故障的目的。
调和聚类\分类算法,用于解决分类和聚类不一致的问题。它通过计算调和矩阵,计算聚类分类是否一致,通过对调和矩阵的不断修正,对聚类和分类的结果进行有效的协调,以达到最大程度上的一致。在电力负荷预测的应用中,该算法具有广泛的适用性,可以应用于其它分类和聚类不一致的场合。
以上对数据挖掘进行的研究,经过模拟、试验和算例验证了算法的有效性,具有重要的理论意义和应用价值。
Data mining is an innovated method of data analysis. It can help people maximize the useful information included in tremendous data, which has become active in artificial intelligence field. Rough set theory is a theory adopted to deal with rough and uncertain knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available, providing a new method for data classification. Although there are numerous methods of rough set and cluster analysis, as the data objects is changing continuously, we have to improve these relevant technologies over time, and propose creative theory in response, meeting the demands of application.
This paper proposes the basic conceptions and attributes of relevant influences in rough set and study the interactions between different attributes, presenting a attributes reduction algorithm based on relevant influences. Through the matrix of relevant influences of attributes, making the relevant influences of attributes as inspiring prerequisite, we effectively delete redundant attributes to gain the reduced sets which reflects the interaction of different attributes. As proved by experiments, the algorithm could obtain the reduction sets composed of attributes with high relevant influences. This conception expands the application range of rough set, presenting a new method for data mining.
Based on the conceptions of relevant influences of rough set, we study dynamic reduction conceptions and methods on the basis of relevant influenced attributes, and calculate the effects of activation stateρ(U) and dormancy stateσ(U) in rough set samples on the attributes reduction sets, whenρ(U)→σ(U), reduce the redundant attributes from reduction sets, whileσ(U)→ρ(U), add the indispensible attributes to the reduction sets, enabling the exchanges of event states be described more effectively by rough sets. This method compensates the deficiency of the previous methods that rough set could only describe static objects.
As intelligence supervising system is the core of industrial automatic control, rough set theory has provided practicable real time decision principles, deducting the weak real time attributes and retaining strong real time ones, to ensure the real time principle of the decision system. The real time method of attributes reduction proposed by this paper has expanded the application of rough set in real time decision systems.
As to the requirements of classification of conditional attributes in decision tables, this paper proposes a reduction algorithm on the basis of attributes classification, which first conducts classification calculation on conditional attributes according to classification functions, then deletes the less important subsets, concluding the classified reduction sets of attributes. As experiments have proved, maintaining the original decision ability constant, this algorithm could deduct parts of the attributes effectively and solves the attributes classification problems.
We have applied our algorithm of attributes classification to the failure diagnosis of electric distribution network and early warning systems of electric interlock network. Studied the attributes choices and rules generation methods of failure diagnosis of electric distribution network, we employed real time attributes reduction and attributes classification reduction to the failure diagnosis systems of electric distribution network. Through calculation of the values of attributes of failure diagnosis and early warning systems of electric interlock network on the basis of electric theory, we have studied the relevant interactions between attributes under the condition of negative charges transforming, obtaining the extents of failures in the network. By the application of reduction algorithm of relevantly influenced attributes, we have observed the changes of attributes and achieved our goal of predicting failures and getting them fixed in time.
Cluster/classification congruity algorithm, proposed in the situation of inconsistence between classification and cluster, is a method to achieve the results the accordance of cluster and classification respectively after calculates the congruity matrix, and coordinates the results effectively by modifying the matrix continuously, in order to achieve the maximum congruity. In the application of prediction of negative charges, this algorithm has a wide application, which could be used on some occasions when the results of cluster and classification are not consistent.
These algorithm mainly conducts research on algorithms of attributes reduction and cluster analysis in rough sets, proposing several creative theories and methods, which also have been applied to electric automatic systems as experiments. As have been proved by experiments, these theories and methods are effective and practicable.
在研究粗糙集关联影响度的基本概念和性质的基础上,通过分析属性之间的相互影响,建立属性的关联影响度矩阵,以属性的关联影响度作为启发条件,有效地删除冗余属性,获得能反映出属性之间相互影响的约简集。实验表明,基于关联影响度的属性约简算法可以得到关联影响度较大的属性组成的约简集。这种概念拓宽了粗糙集的应用范围,为数据挖掘提供了新的方法。
在粗糙集关联影响概念的基础上,对基于关联影响属性动态约简的概念和算法进行了研究,通过计算粗糙集中样本的激活状态ρ(U)和睡眠状态σ(U)对属性约简集的影响,在ρ(U)→σ(U)时,从约简集中将冗余的属性删除,在σ(U)→ρ(U)时,将必要的属性增加到约简集,这些算法和概念是有利于描述事物状态转换的方法。
智能监控系统是工业自动化控制的核心,粗糙集理论为它提供了切实可行的实时决策规则,基于实时性的属性约简算法将弱实时性属性删除,保留强实时性约简集,以保证决策系统的实时性指标,该算法使粗糙集在实时决策系统的应用更加广泛。
属性分类的约简算法能满足决策表中条件属性的分类要求,该算法按照分类函数对条件属性进行分类计算后,将次要的属性子集删除,求得属性分类约简集。实验表明,该算法能够在原决策能力不变的情况下,有效地删除部分属性,解决了属性分类的问题。
将所研究的属性约简算法应用到配电网故障诊断和电网连锁故障诊断预警系统中。在分析和研究配电网故障诊断系统的属性选择和规则产生方法的基础上,将实时性属性约简、属性分类约简算法应用到配电网故障诊断系统中。通过电工理论计算电网连锁故障诊断预警系统的属性值,求出负荷转移情况下属性之间相互产生的关联影响以及线路的故障度。通过关联影响属性约简算法的应用,观察属性的变化,达到预测故障、及时排除故障的目的。
调和聚类\分类算法,用于解决分类和聚类不一致的问题。它通过计算调和矩阵,计算聚类分类是否一致,通过对调和矩阵的不断修正,对聚类和分类的结果进行有效的协调,以达到最大程度上的一致。在电力负荷预测的应用中,该算法具有广泛的适用性,可以应用于其它分类和聚类不一致的场合。
以上对数据挖掘进行的研究,经过模拟、试验和算例验证了算法的有效性,具有重要的理论意义和应用价值。
Data mining is an innovated method of data analysis. It can help people maximize the useful information included in tremendous data, which has become active in artificial intelligence field. Rough set theory is a theory adopted to deal with rough and uncertain knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available, providing a new method for data classification. Although there are numerous methods of rough set and cluster analysis, as the data objects is changing continuously, we have to improve these relevant technologies over time, and propose creative theory in response, meeting the demands of application.
This paper proposes the basic conceptions and attributes of relevant influences in rough set and study the interactions between different attributes, presenting a attributes reduction algorithm based on relevant influences. Through the matrix of relevant influences of attributes, making the relevant influences of attributes as inspiring prerequisite, we effectively delete redundant attributes to gain the reduced sets which reflects the interaction of different attributes. As proved by experiments, the algorithm could obtain the reduction sets composed of attributes with high relevant influences. This conception expands the application range of rough set, presenting a new method for data mining.
Based on the conceptions of relevant influences of rough set, we study dynamic reduction conceptions and methods on the basis of relevant influenced attributes, and calculate the effects of activation stateρ(U) and dormancy stateσ(U) in rough set samples on the attributes reduction sets, whenρ(U)→σ(U), reduce the redundant attributes from reduction sets, whileσ(U)→ρ(U), add the indispensible attributes to the reduction sets, enabling the exchanges of event states be described more effectively by rough sets. This method compensates the deficiency of the previous methods that rough set could only describe static objects.
As intelligence supervising system is the core of industrial automatic control, rough set theory has provided practicable real time decision principles, deducting the weak real time attributes and retaining strong real time ones, to ensure the real time principle of the decision system. The real time method of attributes reduction proposed by this paper has expanded the application of rough set in real time decision systems.
As to the requirements of classification of conditional attributes in decision tables, this paper proposes a reduction algorithm on the basis of attributes classification, which first conducts classification calculation on conditional attributes according to classification functions, then deletes the less important subsets, concluding the classified reduction sets of attributes. As experiments have proved, maintaining the original decision ability constant, this algorithm could deduct parts of the attributes effectively and solves the attributes classification problems.
We have applied our algorithm of attributes classification to the failure diagnosis of electric distribution network and early warning systems of electric interlock network. Studied the attributes choices and rules generation methods of failure diagnosis of electric distribution network, we employed real time attributes reduction and attributes classification reduction to the failure diagnosis systems of electric distribution network. Through calculation of the values of attributes of failure diagnosis and early warning systems of electric interlock network on the basis of electric theory, we have studied the relevant interactions between attributes under the condition of negative charges transforming, obtaining the extents of failures in the network. By the application of reduction algorithm of relevantly influenced attributes, we have observed the changes of attributes and achieved our goal of predicting failures and getting them fixed in time.
Cluster/classification congruity algorithm, proposed in the situation of inconsistence between classification and cluster, is a method to achieve the results the accordance of cluster and classification respectively after calculates the congruity matrix, and coordinates the results effectively by modifying the matrix continuously, in order to achieve the maximum congruity. In the application of prediction of negative charges, this algorithm has a wide application, which could be used on some occasions when the results of cluster and classification are not consistent.
These algorithm mainly conducts research on algorithms of attributes reduction and cluster analysis in rough sets, proposing several creative theories and methods, which also have been applied to electric automatic systems as experiments. As have been proved by experiments, these theories and methods are effective and practicable.
引文
[1]J. R. Quinlan.Programs for Machine Learning. Morgan Kaufmann publishers, 1993,23-26
[2]L. Breiman, J. Frieman, R. Olshen. Classification and Regression Trees. Monterey. CA: Wadsworth Int,1984,32-35
[3]M. Mehta, R. Agrawal,J. Rissanen.SLIQ:A fast scalable classifier for data mining. In Proc.1996 iritl. on Extending Database Technology(EDBT'96), France,1996,67-78
[4]D. Heckerman, A. Mamdani,M Wellman. Real-world applications of Bayesian networks. Communications of ACM,1995,43-47
[5]G. Cooper. Computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence,1990,42:393-405
[6]L. Saul, T. Jaakkola,M. Jordan. Mean field theory for sigmoid belief networks-Journal Artificial Intelligence Research,1996,4:61-76
[7]T. Jaakkola,M. Jordan. Computing upper and lower bounds on likelihoods in intractable networks.On Uncertainty in Artificial Intelligence,1996,340-348
[8]A. Darwiche and G Provan. A practical paradigm for implementing belief-network inference. On Uncertainty in Artificial Intelligence,1996,203-210
[9]D. E. Rumelhart, G E. Hinton,R. J Williams. Learning Internal Representations by Error Propagation. Parallel Distributed Processing, Cambridge,1986,115-119
[10]G. To well and J. Shavlik. Refinement of approximately correct domain theories by knowledge-based neural networks.On Artificial Intelligence,1994,861-866
[11]M. Craven,J. Shavlik. Using sampling and queries to extract rules from trained neural networks. On Machine Learning,1994,31-36
[12]M. Craven, J. Shavlik. Extracting tree-structured representations of trained networks.
Advances in Neural information processing system,1995,45-52
[13]V. N. Vapnik. Statistical learning theory.New York:J.Wiley,1998.51-53
[14]张朝晖,陆玉昌,张拔.利用神经网络发现分类规则.计算机学报,1999,22(1)：108-112
[15]J Macqueen. Some methods for classification and analysis of multivariate observations.1967,281-297
[16]L Kaufman, P J Rousseeuw. Finding Groups in Data:An Introduction to Cluster Analysis.New York:John Wiley & Sons.1990,113-117
[17]R T Ng, J Han. Efficient and efective clustering method for spatial data mining. Very Large Data Bases,1994,144-155
[18]M Ester, H P Kriegel, J Sander. A density-based algorithm for discovering clusters in large spatial databases.Knowledge Discovery and Data Mining (KDD'96),1996,226-231
[19]M Ankerst, M Breunig, H P Kriegel. OPTICS:Ordering points to identify the clustering structure. Management of data (SIGMOD'99),1999,49-60
[20]A Hinneburg, D A Keim. An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining (KDD'98),1998,58-65
[21]W Wang, J Yang, R Muntz. A statistical information grid approach to spatial data mining. Very Large Data Bases (VLDB'97),1997,186-195
[22]R. Agrawal, J. Gehrke, D. Gunopulos. Automatic subspace clustering of highdimensional data for data mining applications. Management of Data (SIGMOD'98),1998,94-105
[23]G Sheikholeslami, S Chaterjee, A Zhang.A multi-resolution clustering approach for very large spatial database. Very Large Data Bases,1998,152-158
[24]D Fisher. Improving inherence through conceptual clustering.1987,461-465
[25]J.Gennar, P.Langley, D.Fisher,Models of incremental concept formation. Artificial
Intelligence,1998,40(1):11-61
[26]P.Cheeseman, J.Stutz.Theory and result. Advances in Knowledge Discovery and Data Mining,2000,153-180
[27]Pizzuti Clara, Talia Domenico. Scalable parallel clustering for mining large data sets. On Knowledge and Data Engineering,2003,15(3):629-641
[28]D.E Rumelhart, D.Zipser.Feature discovery by competitive learning.Congnitive Science.1985,9(1):75-112
[29]T Kohonen. Self-organization and associate memory.Berlin:Springer-Verlag, 1984,86-97
[30]Kohonen T, Improved versions of learning vector quantization.International joint Conference on Networks, San Diego 1990,1:545-550
[31]Cowgill M.C.Harvey, R.J.Watson.A Genetic Algorithm Approach to Cluster Analysis. Computers&Mathematics with Applications,1999,37 (7):99-108
[32]Mali U, Bandyopadhyay S.Genetic algorithm-based clustering technique.Patten Recognition,2000,33 (9):1455-1465
[33]Paolo Corsini, Beatrice Lazzerini, A Fuzzy Relational Clustering Algorithm Based on a Dissimilarity Measure Extracted From Data, IEEE Transactions on Systems, Man,2004,66-78
[34]Jung-Hsien Chiang, Pei-Yi Hao.A new kernel-based fuzzy clustering approach: support vector clustering with cell growing.IEEE Transactions on Fuzzy Systems, 2003,11(4):518-527
[35]Bouguettaya A, Le Viet Q. Data clustering analysis in a multidimensional space. Information Sciences,1998,112:1-4
[36]Strehl A, Ghosh J, Relationship-based clustering and visualization for high-dimensional data mining, INFORMS COMPUT,2003,15(2):208-230
[37]Hen, Chun-Wei Tsai.An Eficient Clustering Algorithm for Large Databases.IEEE International Conference on Systems,2002,5:110-116
[38]Daniel B A, Ping Chen.Using Self-Similarity to Cluster Large Data Sets.Data Mining and Knowledge Discovery,2003,7(2):123-152
[39]Wei Chi-Ping, Lee Yen-Hsien,Hsu Che-Ming.Empirical comparison of fast partitioning-based clustering algorithms for large data sets.Expert Systems with Applications,2003,24(4):351-363
[40]E A Maharaj.Cluster of Time Series.Journal of Classification.2000,17(2):297-314
[41]Z. Pawlak, Rough Sets:theoretical aspects of reasoning about data,Netherlands: Kluwer Academic Publishers,1991,93-102
[42]Chanas S,Kuchta D.Further remarks on the relation between rough and fuzzy sets. Fuzzy Sets and Systems,1992,47:391-394
[43]Yao Y Y. Relational interpretations of neigh borhood operatorsand rough set approximation. Information Sciences,1998,111:239-259
[44]Z. Pawlak. Rough set approach to multi-attribute Journal of Operational Research, 1994,72:443-459
[45]Z.Pawlak. Rough set approach to knowledge-based Journal of Operational Research,1997,99:5-7
[46]M.Kryszkiewicz. Comparative study of alternative types of knowledge reduction in inconsistent systems. International journal of Intelligence Systems,2001,16:105-120
[47]J.Y.Liang, D.Y.Li. Uncertainty and Knowledge Acquisition in information systems. Beijing:Science Press,2005,99-112
[48]W. X. Zhang, J. S. Mi, W. Z. Wu. Approaches to knowledge reductions in inconsistent systems. International Journal of intelligent systems,2003,18:989-1000
[49]A. Skowron, C. Rauszer. The discemibility matrices and functions in information systems. Intelligent Decision Support:Handbook of Applications and Advances of Rough Sets Theory,Kluwer Academic Publisher,1997,331-362
[50]A.Skowron. Rough sets and Boolean reasoning.Granular Computing:An Emerging Paradigm,New York:Physica-Vertag,2001,95-124
[51]A.Skowron, Boolean reasoning for decision rules generation.Proceedings of the Seventh International Symposium ISMIS'93,Lecture Notes in artificial intelligence, Berlin,1993,68:295-305
[52]张文修,吴伟志,梁吉业等.粗糙集理论与方法.北京：科学出版社.2001.1-30
[53]袁修久,张文修.变精度粗糙集下约简和一致决策表约简的关系.模式识别与人工智能.2004,17(2)：196-200
[54]D.Slezak,W.Ziarko.The investigation of the bayesian rough set model. International Journal of Approximate Reasoning,2005,40(1-2):81-91
[55]Q.Liu, F.Jiang, D.Deng.Design and implement for diagnosis sytems of hemorheology on blood viscosity syndrome based gre.In Proceedings of 9th International Conference on Rough Sets.Fuzzy Sets,Data Mining,and Franular Computing, 2003,413-420
[56]M.E.Yahia,R.Mahmod,N.Sulaiman et al. Rough neural expert systems.Expert Systems with Application,2000,18(2):87-99
[57]P.Srinivasan,M.E.Ruiz,D.Kraft et al.Vocabulary mining for information retrieval: rough sets and fuzzy sets. Information Processing and Management,2001,37(1): 15-38
[58]W.Ziarko.Evaluation of probabilistic decision tables.Data Mining,and Granular Computing (RSFDGrC2003),China,2003,189-196
[59]A.Skowron,P.Synak. in information map.In Proceedings of the 8th International Conference on Rough Sets and Current Trends in Computing(RSCTC2002), 2002,453-460
[60]A.Skowron,P.Synak.Reasoning based on information changes in information maps.Data Mining,and Granular Computing (RSFDGrC2003), China,2003,229-236
[61]Y.Y.Yao,Y.Zhao,J.Wang.On reduct construction algorithms.In Proceedings of the First International Conference on Rough Sets and Knowledge Technology (RSKT2006), 2006,297-304
[62]G.Y.Wang,J.Zhao et al.Theoretical study on attribute reduction of rough set theory.In Proceedings of the Third International Conference on Cognitive Informatics (ICCI2004),2004,148-155
[63]王国胤.决策表核属性的计算方法.计算机学报,2003,26(5)：611-615
[64]M.Kryszkiewiez.Comparative studies of alternative type of knowledge reduetion in inconsistent systems.International Journal of Intelligent Systems,2001,16(1): 105-120
[65]M.Inuiguchi.Strueture-based aproaches to attribute reduetion in variable Pricision rough set models. In Proceedings of IEEE International Conference on Granular Computing 2005(IEEE Grc2005),2005,34-39
[66]刘清,刘少辉,郑非.rough逻辑及其在数据约简中的应用.软件学报,2001,12(3)：415-419
[67]G..Wang.Attribute core of decision table. In Proceedings of the 3th Inernational Conference on RoughSets and Current Trends in Computing (RSCTC2002),2002,213-217
[68]R.Felix,T.Ushio.Rough sets based machine learning using a binary discernibility matrix.In Proceedings of 2th International Conference on Intelligent Processing and Manufacturing of Materials(IPMM99),1999,299-305
[69]M.Inulguchi, T.Miyajima.Rough set based rule induction from two decision tables. Accepted by European Journal of Operational Research,2006,102-107
[70]张银奎,廖丽,宋俊等译.数据挖掘原理.北京：机械工业出版社,2003,59-67
[71]J X. H. Hu, N. Cercone. Learning in relational databases:a rough set approach. International Journal of Computational Inteligence,1995,11(2):323-337.
[72]Xu Kali,Zhou Ming,Ren Jianwen.An Object-Oriented Power System Fault Diagnosis Expert System.In:International Conference on Electrical Engineering (ICEE'99),Hong Kong:1999.112-123
[73]文福栓.只利用断路器信息诊断电力系统故障的高级遗传算法.电工技术学报,1996,11(2)：58-64
[74]文福栓.计及警报信息时间特性的故障诊断模型.电力系统自动化,1999,23(17)：6-9
[75]毕天妹,倪以信,杨奇逊.人工智能技术在输电网络故障诊断中的应用述评.电力系统自动化,2000,1-8
[76]段振国,高曙,杨以涵.一种电网故障智能诊断求解模型的研究.中国电机工程学报,1997,17(16)10-15
[77]Application integration at electric utilities System interfaces for distribution management Part 1.99-122
[78]IEC61850 Communication networks and systems in substations Part1.20-34
[79]Ren Wei,Wang Hong-Li,Qiao Yu.Data Mining in Power Market Based on Multi-agent Technology,Power System Technology,2002,17(2):1837-1841
[80]赵冬梅,郑朝明,高曙.配电网的供电优化恢复策略.电网技术.2003.27(5)：67-72
[81]Application integration at electric utilities System interfaces for distribution management Part 3,132-143
[82]柳明,何光宇,卢强.网络分析应用中的公共信息模型.电网技术,2006,30(17)：51-58
[83]刘键,倪建立,毕鹏翔.配电化的模式及馈线开关的选择.电网技术,2000,24(11)：53-55
[84]吴敏,朱锡贵,徐为纲.无信道馈线故障处理技术.电力系统自动化,2000,24(12)：39-41
[85]刘海涛,王晓龙,苏剑.配调一体配网自动化系统的设计与实现.电网技 术,2006,30：668-672
[86]焦振有,焦邵华,刘万顺.配电网馈线系统保护原理及分析.电网技术,2002,26(12)：75-78
[87]万官泉,汪穗峰,张尧等.基于电流安全裕度状态空间截断的可靠性算法.电网技术,2008,32(5)：34-37
[88]别朝红,王锡凡.复杂电力系统一类连锁反应事故可靠性评估初探.电力系统自动化,2001,25(15)：25-28
[89]别朝红,王锡凡.复杂电力系统一类连锁反应事故可靠性评估模型和算法.电力系统自动化,2001,25(20)：30-34
[90]程林,郭永基.可靠性评估中多重故障算法的研究.清华大学学报,2001,41(3)：69-72
[91]朱旭凯,刘文颖,杨以涵等.电网连锁故障演化机理与博弈预防,2008,32(5)：29-32
[92]任建文,周明,李庚银.电网故障信息综合分析及管理系统的研究.电网技术,2002,26(4)4-10
[93]薛禹胜.综合防御由偶然事故演化为大灾难：北美“8.14”大停电的警示.电力系统自动化,2003,27(18)：1-5
[94]薛禹胜.时空协调的大停电防御框架：(一)从孤立防线到综合防御.电力系统自动化.2006,30(1)：8-16
[95]薛禹胜.时空协调的大停电防御框架：(二)广域信息、在线量化分析和自动适应优化控制.电力系统自动化,2006,30(2)：1-10
[96]薛禹胜,时空协调的大停电防御框架：(三)各道防线的内部优化和不同防线之间的协调.电力系统自动化,2006,30(3)：1-10
[97]Hong-Chan Chin.Fault section diagnosis of power system using Fuzzy logic. Power Systems, IEEE Transactions,2003,18(1):245-250
[98]王建会,申展,胡云发.一种高效实用的聚类算法.软件学报,2004,15(05)：697-705
[99]S.Guha,R.Rastogi,K.Shim.an efficient clustering algorithm for large database. Information Systems,2001,26(1):35-58
[100]Pizzuti Clarz,Talia Domenico,Scalable parallel clustering for mining large data sets,IEEE Trans on Knowledge and Data Engineering,2003,15(3):629-641
[101]Cowgill,M.C.Harvey,R. J.Watson. A Genetic Algorithm Approach to Cluster Analysis,.Computers & Mathematics with Applications,1999,37(7):99-108
[102]Mali U,Bandyopadhyay S,Genetic algorithm-based clustering technique,Patten Recognition,2000.33(9):1455-1465
[103]Paolo Corsini,Beatrice Lazzerini,Francesco Marcelloni.A Fuzzy Relational Clustering Algorithm Based on a Dissimilarity Measure Extracted From Data,IEEE Transactions on Systems,2003,332-341
[104]郭宗慧,田风占,靳晓明.数据挖掘教程.北京：清华大学出版社,2005,107-138
[105]张莉,周伟达.核聚类算法.计算机学报,2002,25(6)：587-590
[106]Hen,Chun-Wei Tsai, An Eficient Clustering Algorithm for Large Databases.IEEE International Conference on Systems,2002,5.110-116
[107]Daniel B.A,Ping Chen.Using Self-Similarity to Cluster Large Data Sets.Data Mining and Knowledge Discovery,2003,7(2):123-152
[108]Wei Chi-Ping,Lee Yen-Hsien,Hsu Che-Ming.Empirical comparison of fast partitioning-based clustering algorithms for large data sets.Expert Systems with Applications,2003,24(4):351-363
[109]E.A.Maharaj.Cluster of Time Series，Journal of Classification.2000,17(2):297-315
[110]Guedalia I.D.London M,Werman M,An on-line agglomerative clustering method for non-stationary data.Neural Computation,1999,11(2):128-135
[111]钱卫宁,周傲英.从多角度分析现有聚类算法.软件学报,2002,13(8)：1382-1394
[112]Tianming Hu,Sam Y Sung.Detecting pattern-based outliers.Pattern Recognition Letters,2003,24:3059-3068
[113]R.J.Pell.Multiple outlier detection for multivariate calibration using robust statistical techniques. Chemometrics and Intelligent Laboratory Systems,2000,(52)87-104.
[114]George Kollios.Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets.IEEE Transactions on knowledge and data engineering,2003,15(5):226-237
[115]Maulik U,Bandyopadhyay S,Performance evaluation of some clustering algorithms and validity indices,IEEE Transactions on Patern Analysis and Machine Intelligence, 2002,24(12):1650-1654
[116]Li J,Liu H. Wong L.Use of built-in features in the interpretation of high-dimensional cancer diagnosis data.Proeeedings of the second conferenee on Asia-Pacific bioinformaties,2004,67-74
[117]MeLaehlan G.J., Basford K.E.Inference and Applications to Clustering.NewYork Basel:Marcel Dekker Ine,1988,162-169
[118]Mehta M,Agrawai R,Rissanen J.A Fast Scalable Classifier for Data Mining. 1996,18-32
[119]Meila M.,Heekerman D.,An experimental comparison of model-based clustering methods.Machine Learning,2001,42(2):143-175
[120]Nigma K,Mc Callum A,Thrun S et al,Text classification from labeled and unlabeled documents using EM. Machine Learning,2000,39(3):103-134
[121]Nozaki K.A simple but powerful heuristic method for generating fuzzy rules from numerical data.Fuzzy sets and systems,1997,86(3):251-270
[122]Ohta T,Yamakwa A.,Ichihashi H.et al,Projection pursuit switching regression. Proc.of 5th International Conference on Soft Computing,1998,775-778
[123]Tao,C.W.,Unsupervised fuzzy elustering with multi-center clusters. Fuzzy Sets and Systems,2002,128:305-322
[124]Robert W, Tian Sf. An Efficient Optimality Test of the Fuzzy C-Means Algorithm. Fuzzy sets and systems,2002,86(4):290-297
[125]Taylor J.W,Roberto B.Neural network load forecasting with weather ensemble predictions.IEEE Trans on Power Systems,2001,17(3):626-632
[126]于尔铿,周京阳,刘广一等.能量管理系统(连载).电力系统自动化,1997,21-26
[127]Amjady N.Short-term hourly load forecasting using time-series modeling with peak load estimation capability[J].IEEE Trans on Power Systems,2001,16(3):498-505
[128]Lu Y,Lin Xin.Short-time load forecasting based on grey model and correction system.IEEE/PES Transmission and Distribution Conference & Exhibition:Asia and pacific,China,2005,172-190
[129]Du Tao,Wang Xiuli,Wang Xifan.A Combined model of wavelet and neural nerwork for short term load forecasting.IEEE Trans on Power Systems,2002,16(4):2331-2335
[2]L. Breiman, J. Frieman, R. Olshen. Classification and Regression Trees. Monterey. CA: Wadsworth Int,1984,32-35
[3]M. Mehta, R. Agrawal,J. Rissanen.SLIQ:A fast scalable classifier for data mining. In Proc.1996 iritl. on Extending Database Technology(EDBT'96), France,1996,67-78
[4]D. Heckerman, A. Mamdani,M Wellman. Real-world applications of Bayesian networks. Communications of ACM,1995,43-47
[5]G. Cooper. Computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence,1990,42:393-405
[6]L. Saul, T. Jaakkola,M. Jordan. Mean field theory for sigmoid belief networks-Journal Artificial Intelligence Research,1996,4:61-76
[7]T. Jaakkola,M. Jordan. Computing upper and lower bounds on likelihoods in intractable networks.On Uncertainty in Artificial Intelligence,1996,340-348
[8]A. Darwiche and G Provan. A practical paradigm for implementing belief-network inference. On Uncertainty in Artificial Intelligence,1996,203-210
[9]D. E. Rumelhart, G E. Hinton,R. J Williams. Learning Internal Representations by Error Propagation. Parallel Distributed Processing, Cambridge,1986,115-119
[10]G. To well and J. Shavlik. Refinement of approximately correct domain theories by knowledge-based neural networks.On Artificial Intelligence,1994,861-866
[11]M. Craven,J. Shavlik. Using sampling and queries to extract rules from trained neural networks. On Machine Learning,1994,31-36
[12]M. Craven, J. Shavlik. Extracting tree-structured representations of trained networks.
Advances in Neural information processing system,1995,45-52
[13]V. N. Vapnik. Statistical learning theory.New York:J.Wiley,1998.51-53
[14]张朝晖,陆玉昌,张拔.利用神经网络发现分类规则.计算机学报,1999,22(1)：108-112
[15]J Macqueen. Some methods for classification and analysis of multivariate observations.1967,281-297
[16]L Kaufman, P J Rousseeuw. Finding Groups in Data:An Introduction to Cluster Analysis.New York:John Wiley & Sons.1990,113-117
[17]R T Ng, J Han. Efficient and efective clustering method for spatial data mining. Very Large Data Bases,1994,144-155
[18]M Ester, H P Kriegel, J Sander. A density-based algorithm for discovering clusters in large spatial databases.Knowledge Discovery and Data Mining (KDD'96),1996,226-231
[19]M Ankerst, M Breunig, H P Kriegel. OPTICS:Ordering points to identify the clustering structure. Management of data (SIGMOD'99),1999,49-60
[20]A Hinneburg, D A Keim. An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining (KDD'98),1998,58-65
[21]W Wang, J Yang, R Muntz. A statistical information grid approach to spatial data mining. Very Large Data Bases (VLDB'97),1997,186-195
[22]R. Agrawal, J. Gehrke, D. Gunopulos. Automatic subspace clustering of highdimensional data for data mining applications. Management of Data (SIGMOD'98),1998,94-105
[23]G Sheikholeslami, S Chaterjee, A Zhang.A multi-resolution clustering approach for very large spatial database. Very Large Data Bases,1998,152-158
[24]D Fisher. Improving inherence through conceptual clustering.1987,461-465
[25]J.Gennar, P.Langley, D.Fisher,Models of incremental concept formation. Artificial
Intelligence,1998,40(1):11-61
[26]P.Cheeseman, J.Stutz.Theory and result. Advances in Knowledge Discovery and Data Mining,2000,153-180
[27]Pizzuti Clara, Talia Domenico. Scalable parallel clustering for mining large data sets. On Knowledge and Data Engineering,2003,15(3):629-641
[28]D.E Rumelhart, D.Zipser.Feature discovery by competitive learning.Congnitive Science.1985,9(1):75-112
[29]T Kohonen. Self-organization and associate memory.Berlin:Springer-Verlag, 1984,86-97
[30]Kohonen T, Improved versions of learning vector quantization.International joint Conference on Networks, San Diego 1990,1:545-550
[31]Cowgill M.C.Harvey, R.J.Watson.A Genetic Algorithm Approach to Cluster Analysis. Computers&Mathematics with Applications,1999,37 (7):99-108
[32]Mali U, Bandyopadhyay S.Genetic algorithm-based clustering technique.Patten Recognition,2000,33 (9):1455-1465
[33]Paolo Corsini, Beatrice Lazzerini, A Fuzzy Relational Clustering Algorithm Based on a Dissimilarity Measure Extracted From Data, IEEE Transactions on Systems, Man,2004,66-78
[34]Jung-Hsien Chiang, Pei-Yi Hao.A new kernel-based fuzzy clustering approach: support vector clustering with cell growing.IEEE Transactions on Fuzzy Systems, 2003,11(4):518-527
[35]Bouguettaya A, Le Viet Q. Data clustering analysis in a multidimensional space. Information Sciences,1998,112:1-4
[36]Strehl A, Ghosh J, Relationship-based clustering and visualization for high-dimensional data mining, INFORMS COMPUT,2003,15(2):208-230
[37]Hen, Chun-Wei Tsai.An Eficient Clustering Algorithm for Large Databases.IEEE International Conference on Systems,2002,5:110-116
[38]Daniel B A, Ping Chen.Using Self-Similarity to Cluster Large Data Sets.Data Mining and Knowledge Discovery,2003,7(2):123-152
[39]Wei Chi-Ping, Lee Yen-Hsien,Hsu Che-Ming.Empirical comparison of fast partitioning-based clustering algorithms for large data sets.Expert Systems with Applications,2003,24(4):351-363
[40]E A Maharaj.Cluster of Time Series.Journal of Classification.2000,17(2):297-314
[41]Z. Pawlak, Rough Sets:theoretical aspects of reasoning about data,Netherlands: Kluwer Academic Publishers,1991,93-102
[42]Chanas S,Kuchta D.Further remarks on the relation between rough and fuzzy sets. Fuzzy Sets and Systems,1992,47:391-394
[43]Yao Y Y. Relational interpretations of neigh borhood operatorsand rough set approximation. Information Sciences,1998,111:239-259
[44]Z. Pawlak. Rough set approach to multi-attribute Journal of Operational Research, 1994,72:443-459
[45]Z.Pawlak. Rough set approach to knowledge-based Journal of Operational Research,1997,99:5-7
[46]M.Kryszkiewicz. Comparative study of alternative types of knowledge reduction in inconsistent systems. International journal of Intelligence Systems,2001,16:105-120
[47]J.Y.Liang, D.Y.Li. Uncertainty and Knowledge Acquisition in information systems. Beijing:Science Press,2005,99-112
[48]W. X. Zhang, J. S. Mi, W. Z. Wu. Approaches to knowledge reductions in inconsistent systems. International Journal of intelligent systems,2003,18:989-1000
[49]A. Skowron, C. Rauszer. The discemibility matrices and functions in information systems. Intelligent Decision Support:Handbook of Applications and Advances of Rough Sets Theory,Kluwer Academic Publisher,1997,331-362
[50]A.Skowron. Rough sets and Boolean reasoning.Granular Computing:An Emerging Paradigm,New York:Physica-Vertag,2001,95-124
[51]A.Skowron, Boolean reasoning for decision rules generation.Proceedings of the Seventh International Symposium ISMIS'93,Lecture Notes in artificial intelligence, Berlin,1993,68:295-305
[52]张文修,吴伟志,梁吉业等.粗糙集理论与方法.北京：科学出版社.2001.1-30
[53]袁修久,张文修.变精度粗糙集下约简和一致决策表约简的关系.模式识别与人工智能.2004,17(2)：196-200
[54]D.Slezak,W.Ziarko.The investigation of the bayesian rough set model. International Journal of Approximate Reasoning,2005,40(1-2):81-91
[55]Q.Liu, F.Jiang, D.Deng.Design and implement for diagnosis sytems of hemorheology on blood viscosity syndrome based gre.In Proceedings of 9th International Conference on Rough Sets.Fuzzy Sets,Data Mining,and Franular Computing, 2003,413-420
[56]M.E.Yahia,R.Mahmod,N.Sulaiman et al. Rough neural expert systems.Expert Systems with Application,2000,18(2):87-99
[57]P.Srinivasan,M.E.Ruiz,D.Kraft et al.Vocabulary mining for information retrieval: rough sets and fuzzy sets. Information Processing and Management,2001,37(1): 15-38
[58]W.Ziarko.Evaluation of probabilistic decision tables.Data Mining,and Granular Computing (RSFDGrC2003),China,2003,189-196
[59]A.Skowron,P.Synak. in information map.In Proceedings of the 8th International Conference on Rough Sets and Current Trends in Computing(RSCTC2002), 2002,453-460
[60]A.Skowron,P.Synak.Reasoning based on information changes in information maps.Data Mining,and Granular Computing (RSFDGrC2003), China,2003,229-236
[61]Y.Y.Yao,Y.Zhao,J.Wang.On reduct construction algorithms.In Proceedings of the First International Conference on Rough Sets and Knowledge Technology (RSKT2006), 2006,297-304
[62]G.Y.Wang,J.Zhao et al.Theoretical study on attribute reduction of rough set theory.In Proceedings of the Third International Conference on Cognitive Informatics (ICCI2004),2004,148-155
[63]王国胤.决策表核属性的计算方法.计算机学报,2003,26(5)：611-615
[64]M.Kryszkiewiez.Comparative studies of alternative type of knowledge reduetion in inconsistent systems.International Journal of Intelligent Systems,2001,16(1): 105-120
[65]M.Inuiguchi.Strueture-based aproaches to attribute reduetion in variable Pricision rough set models. In Proceedings of IEEE International Conference on Granular Computing 2005(IEEE Grc2005),2005,34-39
[66]刘清,刘少辉,郑非.rough逻辑及其在数据约简中的应用.软件学报,2001,12(3)：415-419
[67]G..Wang.Attribute core of decision table. In Proceedings of the 3th Inernational Conference on RoughSets and Current Trends in Computing (RSCTC2002),2002,213-217
[68]R.Felix,T.Ushio.Rough sets based machine learning using a binary discernibility matrix.In Proceedings of 2th International Conference on Intelligent Processing and Manufacturing of Materials(IPMM99),1999,299-305
[69]M.Inulguchi, T.Miyajima.Rough set based rule induction from two decision tables. Accepted by European Journal of Operational Research,2006,102-107
[70]张银奎,廖丽,宋俊等译.数据挖掘原理.北京：机械工业出版社,2003,59-67
[71]J X. H. Hu, N. Cercone. Learning in relational databases:a rough set approach. International Journal of Computational Inteligence,1995,11(2):323-337.
[72]Xu Kali,Zhou Ming,Ren Jianwen.An Object-Oriented Power System Fault Diagnosis Expert System.In:International Conference on Electrical Engineering (ICEE'99),Hong Kong:1999.112-123
[73]文福栓.只利用断路器信息诊断电力系统故障的高级遗传算法.电工技术学报,1996,11(2)：58-64
[74]文福栓.计及警报信息时间特性的故障诊断模型.电力系统自动化,1999,23(17)：6-9
[75]毕天妹,倪以信,杨奇逊.人工智能技术在输电网络故障诊断中的应用述评.电力系统自动化,2000,1-8
[76]段振国,高曙,杨以涵.一种电网故障智能诊断求解模型的研究.中国电机工程学报,1997,17(16)10-15
[77]Application integration at electric utilities System interfaces for distribution management Part 1.99-122
[78]IEC61850 Communication networks and systems in substations Part1.20-34
[79]Ren Wei,Wang Hong-Li,Qiao Yu.Data Mining in Power Market Based on Multi-agent Technology,Power System Technology,2002,17(2):1837-1841
[80]赵冬梅,郑朝明,高曙.配电网的供电优化恢复策略.电网技术.2003.27(5)：67-72
[81]Application integration at electric utilities System interfaces for distribution management Part 3,132-143
[82]柳明,何光宇,卢强.网络分析应用中的公共信息模型.电网技术,2006,30(17)：51-58
[83]刘键,倪建立,毕鹏翔.配电化的模式及馈线开关的选择.电网技术,2000,24(11)：53-55
[84]吴敏,朱锡贵,徐为纲.无信道馈线故障处理技术.电力系统自动化,2000,24(12)：39-41
[85]刘海涛,王晓龙,苏剑.配调一体配网自动化系统的设计与实现.电网技 术,2006,30：668-672
[86]焦振有,焦邵华,刘万顺.配电网馈线系统保护原理及分析.电网技术,2002,26(12)：75-78
[87]万官泉,汪穗峰,张尧等.基于电流安全裕度状态空间截断的可靠性算法.电网技术,2008,32(5)：34-37
[88]别朝红,王锡凡.复杂电力系统一类连锁反应事故可靠性评估初探.电力系统自动化,2001,25(15)：25-28
[89]别朝红,王锡凡.复杂电力系统一类连锁反应事故可靠性评估模型和算法.电力系统自动化,2001,25(20)：30-34
[90]程林,郭永基.可靠性评估中多重故障算法的研究.清华大学学报,2001,41(3)：69-72
[91]朱旭凯,刘文颖,杨以涵等.电网连锁故障演化机理与博弈预防,2008,32(5)：29-32
[92]任建文,周明,李庚银.电网故障信息综合分析及管理系统的研究.电网技术,2002,26(4)4-10
[93]薛禹胜.综合防御由偶然事故演化为大灾难：北美“8.14”大停电的警示.电力系统自动化,2003,27(18)：1-5
[94]薛禹胜.时空协调的大停电防御框架：(一)从孤立防线到综合防御.电力系统自动化.2006,30(1)：8-16
[95]薛禹胜.时空协调的大停电防御框架：(二)广域信息、在线量化分析和自动适应优化控制.电力系统自动化,2006,30(2)：1-10
[96]薛禹胜,时空协调的大停电防御框架：(三)各道防线的内部优化和不同防线之间的协调.电力系统自动化,2006,30(3)：1-10
[97]Hong-Chan Chin.Fault section diagnosis of power system using Fuzzy logic. Power Systems, IEEE Transactions,2003,18(1):245-250
[98]王建会,申展,胡云发.一种高效实用的聚类算法.软件学报,2004,15(05)：697-705
[99]S.Guha,R.Rastogi,K.Shim.an efficient clustering algorithm for large database. Information Systems,2001,26(1):35-58
[100]Pizzuti Clarz,Talia Domenico,Scalable parallel clustering for mining large data sets,IEEE Trans on Knowledge and Data Engineering,2003,15(3):629-641
[101]Cowgill,M.C.Harvey,R. J.Watson. A Genetic Algorithm Approach to Cluster Analysis,.Computers & Mathematics with Applications,1999,37(7):99-108
[102]Mali U,Bandyopadhyay S,Genetic algorithm-based clustering technique,Patten Recognition,2000.33(9):1455-1465
[103]Paolo Corsini,Beatrice Lazzerini,Francesco Marcelloni.A Fuzzy Relational Clustering Algorithm Based on a Dissimilarity Measure Extracted From Data,IEEE Transactions on Systems,2003,332-341
[104]郭宗慧,田风占,靳晓明.数据挖掘教程.北京：清华大学出版社,2005,107-138
[105]张莉,周伟达.核聚类算法.计算机学报,2002,25(6)：587-590
[106]Hen,Chun-Wei Tsai, An Eficient Clustering Algorithm for Large Databases.IEEE International Conference on Systems,2002,5.110-116
[107]Daniel B.A,Ping Chen.Using Self-Similarity to Cluster Large Data Sets.Data Mining and Knowledge Discovery,2003,7(2):123-152
[108]Wei Chi-Ping,Lee Yen-Hsien,Hsu Che-Ming.Empirical comparison of fast partitioning-based clustering algorithms for large data sets.Expert Systems with Applications,2003,24(4):351-363
[109]E.A.Maharaj.Cluster of Time Series，Journal of Classification.2000,17(2):297-315
[110]Guedalia I.D.London M,Werman M,An on-line agglomerative clustering method for non-stationary data.Neural Computation,1999,11(2):128-135
[111]钱卫宁,周傲英.从多角度分析现有聚类算法.软件学报,2002,13(8)：1382-1394
[112]Tianming Hu,Sam Y Sung.Detecting pattern-based outliers.Pattern Recognition Letters,2003,24:3059-3068
[113]R.J.Pell.Multiple outlier detection for multivariate calibration using robust statistical techniques. Chemometrics and Intelligent Laboratory Systems,2000,(52)87-104.
[114]George Kollios.Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets.IEEE Transactions on knowledge and data engineering,2003,15(5):226-237
[115]Maulik U,Bandyopadhyay S,Performance evaluation of some clustering algorithms and validity indices,IEEE Transactions on Patern Analysis and Machine Intelligence, 2002,24(12):1650-1654
[116]Li J,Liu H. Wong L.Use of built-in features in the interpretation of high-dimensional cancer diagnosis data.Proeeedings of the second conferenee on Asia-Pacific bioinformaties,2004,67-74
[117]MeLaehlan G.J., Basford K.E.Inference and Applications to Clustering.NewYork Basel:Marcel Dekker Ine,1988,162-169
[118]Mehta M,Agrawai R,Rissanen J.A Fast Scalable Classifier for Data Mining. 1996,18-32
[119]Meila M.,Heekerman D.,An experimental comparison of model-based clustering methods.Machine Learning,2001,42(2):143-175
[120]Nigma K,Mc Callum A,Thrun S et al,Text classification from labeled and unlabeled documents using EM. Machine Learning,2000,39(3):103-134
[121]Nozaki K.A simple but powerful heuristic method for generating fuzzy rules from numerical data.Fuzzy sets and systems,1997,86(3):251-270
[122]Ohta T,Yamakwa A.,Ichihashi H.et al,Projection pursuit switching regression. Proc.of 5th International Conference on Soft Computing,1998,775-778
[123]Tao,C.W.,Unsupervised fuzzy elustering with multi-center clusters. Fuzzy Sets and Systems,2002,128:305-322
[124]Robert W, Tian Sf. An Efficient Optimality Test of the Fuzzy C-Means Algorithm. Fuzzy sets and systems,2002,86(4):290-297
[125]Taylor J.W,Roberto B.Neural network load forecasting with weather ensemble predictions.IEEE Trans on Power Systems,2001,17(3):626-632
[126]于尔铿,周京阳,刘广一等.能量管理系统(连载).电力系统自动化,1997,21-26
[127]Amjady N.Short-term hourly load forecasting using time-series modeling with peak load estimation capability[J].IEEE Trans on Power Systems,2001,16(3):498-505
[128]Lu Y,Lin Xin.Short-time load forecasting based on grey model and correction system.IEEE/PES Transmission and Distribution Conference & Exhibition:Asia and pacific,China,2005,172-190
[129]Du Tao,Wang Xiuli,Wang Xifan.A Combined model of wavelet and neural nerwork for short term load forecasting.IEEE Trans on Power Systems,2002,16(4):2331-2335