采用量子进化算法学习的贝叶斯网络及其应用研究
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摘要
量子进化算法是量子计算和进化算法相融合的产物,是一种新颖的智能优化算法。本文提出一种改进的量子进化算法,不同于传统量子算法的量子比特编码方法,而是采用实数编码,并引入轮盘赌的模拟退火优化策略。此外,该算法在求解贝叶斯网络结构优化问题中增加了遗传优化算子和修改非法图算子,与传统算法相比具有一定的优越性。同时本文还建立了适用于电表故障诊断的贝叶斯网络模型,并使用本文所提算法对该模型进行优化求解,取得了良好的诊断精度。
本文工作主要包含以下几个方面:
首先,深入研究了量子进化算法的原理和特征,阐述了贝叶斯网络的基本概念和主要研究内容,详细介绍了贝叶斯网络应用于故障诊断技术的优势和诊断技术的发展趋势。
其次,针对进化算法的特征和研究现状,提出了一种改进的实数编码量子进化算法。基于对实数编码的量子进化算法的分析,以及借鉴其他优化算法的优点,本文采用多种算法相融合的方法,对实数编码量子进化算法进行改进。此外,本文还对针对该算法建立了相应的数学模型,并进行了逻辑推导和一致收敛性证明。
再次,研究了贝叶斯网络的参数学习和结构学习的原理,并提出一种基于改进实数编码量子进化算法与遗传算法相结合的贝叶斯网络结构学习算法。该算法先将实数编码转换成二进制编码,通过遗传变异、模拟退火策略、修改非法图等算子来进行网络学习,并得到简洁高效的网络结构。本文最后利用Matlab软件对所提方法来进行仿真验证,结果证明该算法可提高贝叶斯网络学习的收敛速度和精度。
最后,介绍了智能电表的故障特点,并针对智能电表故障诊断问题建立了基于改进进化算法的贝叶斯网络故障诊断模型。为证明所提模型的有效性,本文最终利用MSBNx软件仿真证明了其处理系统故障诊断问题的优越性。
Quantum Evolutionary Algorithm (QEA) is one kind of new intelligent optimization algorithm, which is a combination of the quantum computing and evolutionary algorithm. In this thesis, an improved Quantum-inspired Evolutionary Algorithm (IQEA) is proposed. Compared with traditional QEA, it is not based on quantum-bits coding but based on real coding whilst the simulated annealing(SA) with roulette strategy is employed. Furthermore, since two new operators named optimized mutation and illegal figure modification are proposed and added in the proposed algorithm, it will be superior in solving the problem of Bayesian Network (BN) structure learning. In addition, a Bayesian Net based fault diagnosis model is proposed for electric meter fault diagnosis. The proposed algorithm will be utilized for optimizing and solving this model and a good accuracy of diagnosis is achieved.
The main content of this thesis is as follows:
Firstly, the principium and characteristics of QEA are deeply investigated while the principium and related research about Bayesian Network are discussed. In addition, the advantages of BN in fault diagnosis and the trend of related technology development are introduced.
Secondly, based on the characteristics and current research about evolution algorithm (EA), a real coding based Quantum-inspired Evolutionary Algorithm (RQEA) is proposed. By using the advantages of other algorithms for reference, RQEA is significantly improved by using a synthesis of different algorithms. Furthermore, the logical derivation is introduced while the convergence of algorithm is verified mathematically.
Thirdly, the principium of BN parameters learning and structure learning is introduced and an improved Quantum-inspired Evolutionary Algorithm for BN structure learning is proposed. The coding of proposed IQEA is firstly transformed from real coding to binary coding and a compact net structure is obtained by network learning operators such as genetic mutation, simulated annealing(SA) with roulette strategy and illegal figure modification. The proposed algorithm is verified at last by using Matlab software, which shows it can accelerate the convergence speed of BN learning and improve the training accuracy.
Finally, the key feature of the fault of intelligent electric meter is introduced and a BN fault diagnosis model based on IQEA is proposed for diagnosing. For verifying the effectiveness of the proposed model, its advantages in solving system fault diagnosis are verified by MSBNx software.
本文工作主要包含以下几个方面:
首先,深入研究了量子进化算法的原理和特征,阐述了贝叶斯网络的基本概念和主要研究内容,详细介绍了贝叶斯网络应用于故障诊断技术的优势和诊断技术的发展趋势。
其次,针对进化算法的特征和研究现状,提出了一种改进的实数编码量子进化算法。基于对实数编码的量子进化算法的分析,以及借鉴其他优化算法的优点,本文采用多种算法相融合的方法,对实数编码量子进化算法进行改进。此外,本文还对针对该算法建立了相应的数学模型,并进行了逻辑推导和一致收敛性证明。
再次,研究了贝叶斯网络的参数学习和结构学习的原理,并提出一种基于改进实数编码量子进化算法与遗传算法相结合的贝叶斯网络结构学习算法。该算法先将实数编码转换成二进制编码,通过遗传变异、模拟退火策略、修改非法图等算子来进行网络学习,并得到简洁高效的网络结构。本文最后利用Matlab软件对所提方法来进行仿真验证,结果证明该算法可提高贝叶斯网络学习的收敛速度和精度。
最后,介绍了智能电表的故障特点,并针对智能电表故障诊断问题建立了基于改进进化算法的贝叶斯网络故障诊断模型。为证明所提模型的有效性,本文最终利用MSBNx软件仿真证明了其处理系统故障诊断问题的优越性。
Quantum Evolutionary Algorithm (QEA) is one kind of new intelligent optimization algorithm, which is a combination of the quantum computing and evolutionary algorithm. In this thesis, an improved Quantum-inspired Evolutionary Algorithm (IQEA) is proposed. Compared with traditional QEA, it is not based on quantum-bits coding but based on real coding whilst the simulated annealing(SA) with roulette strategy is employed. Furthermore, since two new operators named optimized mutation and illegal figure modification are proposed and added in the proposed algorithm, it will be superior in solving the problem of Bayesian Network (BN) structure learning. In addition, a Bayesian Net based fault diagnosis model is proposed for electric meter fault diagnosis. The proposed algorithm will be utilized for optimizing and solving this model and a good accuracy of diagnosis is achieved.
The main content of this thesis is as follows:
Firstly, the principium and characteristics of QEA are deeply investigated while the principium and related research about Bayesian Network are discussed. In addition, the advantages of BN in fault diagnosis and the trend of related technology development are introduced.
Secondly, based on the characteristics and current research about evolution algorithm (EA), a real coding based Quantum-inspired Evolutionary Algorithm (RQEA) is proposed. By using the advantages of other algorithms for reference, RQEA is significantly improved by using a synthesis of different algorithms. Furthermore, the logical derivation is introduced while the convergence of algorithm is verified mathematically.
Thirdly, the principium of BN parameters learning and structure learning is introduced and an improved Quantum-inspired Evolutionary Algorithm for BN structure learning is proposed. The coding of proposed IQEA is firstly transformed from real coding to binary coding and a compact net structure is obtained by network learning operators such as genetic mutation, simulated annealing(SA) with roulette strategy and illegal figure modification. The proposed algorithm is verified at last by using Matlab software, which shows it can accelerate the convergence speed of BN learning and improve the training accuracy.
Finally, the key feature of the fault of intelligent electric meter is introduced and a BN fault diagnosis model based on IQEA is proposed for diagnosing. For verifying the effectiveness of the proposed model, its advantages in solving system fault diagnosis are verified by MSBNx software.
引文
[1]李士勇,李盼池.量子计算与量子优化算法[M] .哈尔滨:哈尔滨大学出版社,2007.
[2] Shor P W. Algorithms for quantum computer[C] .Discrete logarithms and factoring. Proc.of the 35th Annual symp. on Foundations of computer science, New York, USA:IEEE Computer Society Pree,1994,11:124-134.
[3] Grover L K.A fast quantum mechanical algorithm for database search[C] Proc.of the 28th Annual ACM symp. on theory of Computing. New York, USA:ACM Pree,1996,6:212-219.
[4]罗江华.贝叶斯网络在机械故障诊断中的应用:[重庆大学硕士学位论文].重庆,重庆大学.2006:9-32
[5] Narayanan A, Moore M. Quantum-inspired genetic algorithm[A]. Proceedings of IEEE International Conference on Evolutionary Computation[C]. Piscataway: IEEE Press, 1999: 61-66
[6] Han K H, Kim J H. Genetic quantum algorithm and its application to combinatorial optimization problem[A]. Proceedings of the 2000 IEEE Congress on Evolutionary Computation[C] USA: IEEE Press, 2000.1354-1360
[7] Han K H , Kim J H. Quantum-inspired evolutionary algorithm for a class of combination optimization[J],IEEE Trans evolutionary Computation,2002,6(6):580-593
[8] Zhang G X, Jin W D, Hu L Z. A novel parallel quantum genetic algorithm and its application of the fourth international conference on Parallel and Distribution Computing ,Applications and Technologies,2003,693-697.
[9] Talbi H, Draa A, Batouche M. A new quantum-inspired genetic algorithm for solving the travelling salesman problem[C] Proceedings of the International Conference on Industrial Technology,2004,3:1192-1197.
[10] Chen H, Zhang J H, Zhang C. Chaos updating rotated gates quantum-inspired genetic algorithm[C] Proceedings of the International Conference on Communications, Cuircuits and system ,2004,2:1108-1112.
[11] Yang S Y, Wang M, Jiao L C. A genetic algorithm based on quantum chromosome[C] Proceedings of the 7th International Conference on signal Processing, 2004, 2: 1622-1625.
[12] WANG L, TANG F,WU H. Hybrid genetic algorithm based on quantumcomputing for numerical optimization and parameter estimation[J].applied mathematics and computation,2005,171:1141-1156.
[13] Khorsand A R, Akbarzadeh M R. Quantum gate optimization in meta-level genetic quantum algorithm[C] 2005 IEEE International Conference on systems, man and cybernetics,2005,4:3055-3062
[14]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
[15] Dreo J, Siarry P. An ant colony algorithm aimed at dynamic continuous optimization[J].Applied Mathematics and computation,2006,181(1):457-467.
[16]黄友平.贝叶斯网络研究:[博士学位论文].北京.中国科学院计算机技术研究所.2005:6-07
[17]李玉玲等,高级概率传播网研究及应用[J].微计算机信息,2008,24:141-143
[18]张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006
[19] FRIEDMAN N, GEIGER D, GOLDSZMIDT M. Bayesian network classifiers[J]. Machine Learning, 1997, 29 (2/3):131- 163.
[20] Cooper G F, Herskovits E. A bayesian method for the induction of probabilistic network s from data [J]. Machine Learning,1992,9:309-347
[21] Lauritzen S L. The EM algorithm for graphical association models with missing data [J]. Computational Statistics and Data Analysis, 1995, 19 (2):191-201
[22]李晓毅,徐兆棣,孙笑微.贝叶斯网络参数学习研究[J],沈阳农业大学学报;2007,38:125-128.
[23]俞奎,王浩等.并行的贝叶斯网络参数学习.[J]小型微型计算机系统.2007,11:1972-1975
[24] Chickering D M. Optimal structure identification with greedy search[J].Journal of Machine Learning Research,2002,11(3):507—554
[25] Shetty S, Song M. Structure learning of Bayesian network using a semantic genetic algorithm—based approach[C]Proceedings of the 3rd International Conference on Information Technology:Research and Education, Hsinchu,2005:454—458
[26] Sahin F, Yavuz M, Amavu Z. et al. Fault diagnosis for airplane engines using Bayesian network and distributed particle swam optimization[J].Parallel Computing,2007, 11(33):124—143
[27]李海军,马登武等.贝叶斯网络理论在装备故障诊断中的应用[M] .北京:国防工业出版社,2009.
[28]朱大奇,于盛林.基于知识的故障诊断方法综述[J].安徽工业大学学报,2002,19:9-12.
[29] FRANK P M,DING X. Survey of robust residual generation and evaluation methods in observers-based fault detection system[J].J Process Control , 1997 , 7(6) :403-424
[30] ESCOBET T , FEROLDI D ,DE L IRA S. Model based fault diagnosis in pem- fuel cell systems[J ] . Journal Of Power Sources ,2008 (14)
[31]鄂加强.智能故障诊断及其应用[M] .长沙:湖南大学出版社,2006.
[32]金鑫等.智能故障诊断技术研究综述[J].理论与方法.2009,7:30-32.
[33]王凌.量子进化算法研究进展[J].控制与决策.2008,12:1321-1326
[34]娄素华.量子进化算法在电力系统无功优化中的应用.继电器.2005,33:30-35
[35]许昆等.改进量子粒子群多目标优化算法.计算机工程与设计.2009,30:164-167
[36]肖秦琨等.动态贝叶斯网络推理学习理论与应用.[M].北京:国防工业出版社,2007.
[37]罗江华.贝叶斯网络在机械故障诊断中的应用研究:[硕士学位论文].重庆.重庆大学.2006:4-01
[38]郭富强.智能故障诊断方法综述.陕西广播电视大学学报.2009,11:87-90
[39]王华伟,等.基于贝叶斯网络的复杂系统故障诊断.计算机集成制造系统, 2004,10(2):230-23
[40]王小平、曹立明.遗传算法——理论、应用与软件实现[M].西安.西安交通大学出版社.2002
[41]段海滨等,蚁群算法原理及其应用[M].北京.科学出版社.2006
[42]焦李成.免疫优化计算,学习与识别[M].北京.科学出版社.2006
[43]曾建潮.微粒群算法[M].北京.科学出版社.2004
[44]高尚.群智能算法与应用[M].北京.中国水利水电出版社.2004
[45]康立山.非数值并行算法第一册——模拟退火算法[M].北京.科学出版社.1994
[46]高辉等.实数编码量子进化算法[J].控制与决策.2008,23(1):87-90
[47] Andre V. Quantum-Inspired Evolutionary Algorithm for Numerical Optimization .[J]. IEEE Congress on Evolutionary Computation.2006,7:16-21
[48]覃朝勇,郑建国,朱佳俊.一种实数编码量子进化算法及其收敛性.[J].控制与决策, 2009, 24(6): 854-858.
[49]盛骤,谢式千,潘承毅.概率论与数理统计[M] .北京,高等教育出版社, 2002
[50] Yao X, Liu Y, Lin G. Evolutionary programming made faster[J]. IEEE Trans on Evolutionary Computation ,1999 , 3 (2) : 82-102.
[51] Tu Z.A robust stochastic genetic algorithm(STGA) for global numericaloptimization[J].IEEE Trans on Evolutionary Computation 2004 , 8 (5) : 456-470.
[52]厉海涛等.贝叶斯网络推理算法综述.系统工程与电子技术[J].2008,30:935-939
[53]许丽佳等.基于贝叶斯网络的复杂系统故障预测[J].系统工程与电子技术[J].2008,30:780-784
[54]马壮等.贝叶斯网络结构学习的研究现状及发展趋势[J].合肥工业大学学报.2005,28:833-837
[55]高丽.一种用于汽车发动机故障诊断的贝叶斯网络模型[J].计算机测量与控制.2009,17:830-832
[2] Shor P W. Algorithms for quantum computer[C] .Discrete logarithms and factoring. Proc.of the 35th Annual symp. on Foundations of computer science, New York, USA:IEEE Computer Society Pree,1994,11:124-134.
[3] Grover L K.A fast quantum mechanical algorithm for database search[C] Proc.of the 28th Annual ACM symp. on theory of Computing. New York, USA:ACM Pree,1996,6:212-219.
[4]罗江华.贝叶斯网络在机械故障诊断中的应用:[重庆大学硕士学位论文].重庆,重庆大学.2006:9-32
[5] Narayanan A, Moore M. Quantum-inspired genetic algorithm[A]. Proceedings of IEEE International Conference on Evolutionary Computation[C]. Piscataway: IEEE Press, 1999: 61-66
[6] Han K H, Kim J H. Genetic quantum algorithm and its application to combinatorial optimization problem[A]. Proceedings of the 2000 IEEE Congress on Evolutionary Computation[C] USA: IEEE Press, 2000.1354-1360
[7] Han K H , Kim J H. Quantum-inspired evolutionary algorithm for a class of combination optimization[J],IEEE Trans evolutionary Computation,2002,6(6):580-593
[8] Zhang G X, Jin W D, Hu L Z. A novel parallel quantum genetic algorithm and its application of the fourth international conference on Parallel and Distribution Computing ,Applications and Technologies,2003,693-697.
[9] Talbi H, Draa A, Batouche M. A new quantum-inspired genetic algorithm for solving the travelling salesman problem[C] Proceedings of the International Conference on Industrial Technology,2004,3:1192-1197.
[10] Chen H, Zhang J H, Zhang C. Chaos updating rotated gates quantum-inspired genetic algorithm[C] Proceedings of the International Conference on Communications, Cuircuits and system ,2004,2:1108-1112.
[11] Yang S Y, Wang M, Jiao L C. A genetic algorithm based on quantum chromosome[C] Proceedings of the 7th International Conference on signal Processing, 2004, 2: 1622-1625.
[12] WANG L, TANG F,WU H. Hybrid genetic algorithm based on quantumcomputing for numerical optimization and parameter estimation[J].applied mathematics and computation,2005,171:1141-1156.
[13] Khorsand A R, Akbarzadeh M R. Quantum gate optimization in meta-level genetic quantum algorithm[C] 2005 IEEE International Conference on systems, man and cybernetics,2005,4:3055-3062
[14]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
[15] Dreo J, Siarry P. An ant colony algorithm aimed at dynamic continuous optimization[J].Applied Mathematics and computation,2006,181(1):457-467.
[16]黄友平.贝叶斯网络研究:[博士学位论文].北京.中国科学院计算机技术研究所.2005:6-07
[17]李玉玲等,高级概率传播网研究及应用[J].微计算机信息,2008,24:141-143
[18]张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006
[19] FRIEDMAN N, GEIGER D, GOLDSZMIDT M. Bayesian network classifiers[J]. Machine Learning, 1997, 29 (2/3):131- 163.
[20] Cooper G F, Herskovits E. A bayesian method for the induction of probabilistic network s from data [J]. Machine Learning,1992,9:309-347
[21] Lauritzen S L. The EM algorithm for graphical association models with missing data [J]. Computational Statistics and Data Analysis, 1995, 19 (2):191-201
[22]李晓毅,徐兆棣,孙笑微.贝叶斯网络参数学习研究[J],沈阳农业大学学报;2007,38:125-128.
[23]俞奎,王浩等.并行的贝叶斯网络参数学习.[J]小型微型计算机系统.2007,11:1972-1975
[24] Chickering D M. Optimal structure identification with greedy search[J].Journal of Machine Learning Research,2002,11(3):507—554
[25] Shetty S, Song M. Structure learning of Bayesian network using a semantic genetic algorithm—based approach[C]Proceedings of the 3rd International Conference on Information Technology:Research and Education, Hsinchu,2005:454—458
[26] Sahin F, Yavuz M, Amavu Z. et al. Fault diagnosis for airplane engines using Bayesian network and distributed particle swam optimization[J].Parallel Computing,2007, 11(33):124—143
[27]李海军,马登武等.贝叶斯网络理论在装备故障诊断中的应用[M] .北京:国防工业出版社,2009.
[28]朱大奇,于盛林.基于知识的故障诊断方法综述[J].安徽工业大学学报,2002,19:9-12.
[29] FRANK P M,DING X. Survey of robust residual generation and evaluation methods in observers-based fault detection system[J].J Process Control , 1997 , 7(6) :403-424
[30] ESCOBET T , FEROLDI D ,DE L IRA S. Model based fault diagnosis in pem- fuel cell systems[J ] . Journal Of Power Sources ,2008 (14)
[31]鄂加强.智能故障诊断及其应用[M] .长沙:湖南大学出版社,2006.
[32]金鑫等.智能故障诊断技术研究综述[J].理论与方法.2009,7:30-32.
[33]王凌.量子进化算法研究进展[J].控制与决策.2008,12:1321-1326
[34]娄素华.量子进化算法在电力系统无功优化中的应用.继电器.2005,33:30-35
[35]许昆等.改进量子粒子群多目标优化算法.计算机工程与设计.2009,30:164-167
[36]肖秦琨等.动态贝叶斯网络推理学习理论与应用.[M].北京:国防工业出版社,2007.
[37]罗江华.贝叶斯网络在机械故障诊断中的应用研究:[硕士学位论文].重庆.重庆大学.2006:4-01
[38]郭富强.智能故障诊断方法综述.陕西广播电视大学学报.2009,11:87-90
[39]王华伟,等.基于贝叶斯网络的复杂系统故障诊断.计算机集成制造系统, 2004,10(2):230-23
[40]王小平、曹立明.遗传算法——理论、应用与软件实现[M].西安.西安交通大学出版社.2002
[41]段海滨等,蚁群算法原理及其应用[M].北京.科学出版社.2006
[42]焦李成.免疫优化计算,学习与识别[M].北京.科学出版社.2006
[43]曾建潮.微粒群算法[M].北京.科学出版社.2004
[44]高尚.群智能算法与应用[M].北京.中国水利水电出版社.2004
[45]康立山.非数值并行算法第一册——模拟退火算法[M].北京.科学出版社.1994
[46]高辉等.实数编码量子进化算法[J].控制与决策.2008,23(1):87-90
[47] Andre V. Quantum-Inspired Evolutionary Algorithm for Numerical Optimization .[J]. IEEE Congress on Evolutionary Computation.2006,7:16-21
[48]覃朝勇,郑建国,朱佳俊.一种实数编码量子进化算法及其收敛性.[J].控制与决策, 2009, 24(6): 854-858.
[49]盛骤,谢式千,潘承毅.概率论与数理统计[M] .北京,高等教育出版社, 2002
[50] Yao X, Liu Y, Lin G. Evolutionary programming made faster[J]. IEEE Trans on Evolutionary Computation ,1999 , 3 (2) : 82-102.
[51] Tu Z.A robust stochastic genetic algorithm(STGA) for global numericaloptimization[J].IEEE Trans on Evolutionary Computation 2004 , 8 (5) : 456-470.
[52]厉海涛等.贝叶斯网络推理算法综述.系统工程与电子技术[J].2008,30:935-939
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