【作者】 张亮；

【导师】 章兢；

【作者基本信息】 湖南大学 ， 控制科学与工程， 2011， 硕士

【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。更多还原

【Abstract】 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.更多还原