基于分布估计算法的磁浮列车故障综合评判

针对使用传统模糊综合评判方法进行故障级别评判时模型参数难以确定的问题,提出一种基于分布估计算法(EDA)的模糊综合故障评判方法.该方法利用EDA进行模糊评判模型的进化学习,能有效实现模糊模型参数的自动优化,并具有模型易于理解,计算效率高的优点.通过对磁浮列车悬浮系统的仿真实验,结果显示基于分布估计算法的模糊故障综合评判方法能获得优于传统进化算法和其他机器学习方法的评判效果,具有较好的应用价值.

     

分布估计算法研究进展

作为一种新颖的基于概率模型的进化算法,近年来分布估计算法（EDA）得到了广泛的研究和发展.在介绍分布估计算法原理和特点的基础上,重点综述了近些年分布估计算法的研究进展,包括改进概率模型、保持种群多样性以及设计混合算法,进而总结了分布估计算法在理论及应用方面的研究现状,最后提出了有待进一步研究的若干方向和内容.     

组合分布估计和差分进化的多目标优化算法

为了提高多目标优化算法的收敛能力及求解精度,提出了一种组合分布估计和差分进化的多目标优化算法.该方法用分布估计算法和差分进化算法共同生成种群中的粒子,利用选择因子来控制每个粒子的产生方式,并且根据迭代次数的增加来改变2种算法的使用比例,搜索初期利用分布估计算法进行快速定位,然后用差分进化算法进行精确搜索.并对差分进化算法的变异因子进行了改进,定义了一个可变的变异因子,来控制不同搜索时期中差分进化算法的变异范围.用4个测试函数对算法进行了仿真测试,并同NSGA-Ⅱ和RM-MEDA进行了比较.实验结果表明,该算法具有良好的收敛性和分布性,并且效果稳定.     

利用Copula函数估计概率模型并采样的分布估计算法

将Copula理论引入分布估计算法的研究中,并在估计概率模型时分两个步骤进行：1）估计各变量的边缘分布函数;2）构造经验copula函数或正态Copula函数．根据Copula函数和各边缘分布进行采样,在简化估计模型运算复杂度的同时,充分反映了变量之间的关系．仿真实验验证了该算法的可行性和有效性．     

一种基于混合因子分析的分布估计算法

提出了一种基于混合因子分析的分布估计算法．首先用次胜者受罚的竞争学习算法对选出的最优个体集合聚类,然后对每个类用因子分析模型进行分布信息的估计．为了保持种群的多样性,算法保留那些具有较好适应值并且与所选的最优个体集合较远的个体,并利用聚类的参数来减少计算量．试验结果证实了算法的性能．     

二阶卡尔曼滤波分布估计算法

分布估计算法由于其较强的理论基础已成为进化计算研究的新热点．从卡尔曼滤波的角度来看,它的作甩实际上是一个递归滤波器,但作用在一个种群上的分布估计算法相当于只有一个信息源．因此,该文利用信息融合的思想,将种群分成若干子种群,各子种群独立地使用二阶分布估计算法来估计其状态,这样就可从多个信息源获得信息．然后用卡尔曼滤波器将这多个信息源的信息相融合,以产生更准确的估计,并将估计信息反馈到各子种群中．实验结果表明,相对于已有的二阶分布估计算法,该文算法的稳定性和全局搜索能力都得到了很大提高,从而说明了该文算法的有效性．     

基于分布估计算法的模糊分类方法

提出了基于分布估计算法的模糊分类建模方法,该方法基于Apriori原理生成初始模糊规则集,并且以匹茨堡型的二进制编码方式对模糊规则集编码,基于双变量相关的MIMIC (mutual information maximization for input clustering)分布估计算法从初始规则集中自动抽取模糊规则.通过在Iris,Pima,Wine这3个标准数据集的仿真实验表明,该方法比基于遗传算法的模糊分类器在准确率和解释性方面更有效.     

动态环境下基于近似动态规划的分布估计算法研究

论文重点讨论了分布估计算法的理论研究。首先,抽取出分布估计算法的核心思想,然后旨在使用EDA算法解决复杂优化问题,提出基于近似动态规划的分布估计算法。通过Agent与环境的交互,将近似动态规划引入到进化计算中,获得概率模型并进行适应性的更新。测试函数使用六个经典的对比实验,结果表明本算法的鲁棒性,运行时间短并具有较强的全局搜索能力,可以作为解决函数优化问题的有效解决算法。     

基于模拟退火的改进分布估计算法研究

针对分布估计算法(EDA)局部搜索能力弱、迭代后期不易跳出局部最优解的缺点,提出一种基于模拟退火的改进分布估计算法(SA-EDA)。SA-EDA在迭代初期保留EDA的优点,能够快速收敛,全局寻优能力强;在迭代后期算法停滞时则采用模拟退火机制,利用Metropolis接受准则能以一定概率接受较劣解的特点,增加种群多样性,使算法跳出当前最优,并进一步搜索全局最优解。通过六个测试函数的检验结果表明,与EDA和粒子群算法(PSO)相比,SA-EDA收敛精度更好,稳定性更强,并具备比EDA更快的收敛速度,寻优性能更佳。     

自适应混合多目标分布估计进化算法

针对多目标分布估计算法全局收敛性较弱的缺陷,提出了一种自适应混合多目标分布估计进化算法。其基本思想是:在多目标分布估计算法中引入全局收敛性较强的差分进化算法,当函数变化率较大时,用分布估计算法产生新种群;当函数变化率较小即算法可能陷入局部收敛时,用差分进化算法产生新种群。理论分析和数值实验结果表明,这种混合算法不仅具有良好的全局收敛性,而且解的分布性和均匀性较没有考虑目标函数变化率的混合多目标分布估计算法也有了一定程度的提高。     

Inexact graph matching by means of estimation of distribution algorithms

Estimation of distribution algorithms (EDAs) are a quite recent topic in optimization techniques. They combine two technical disciplines of soft computing methodologies: probabilistic reasoning and evolutionary computing. Several algorithms and approaches have already been proposed by different authors, but up to now there are very few papers showing their potential and comparing them to other evolutionary computational methods and algorithms such as genetic algorithms (GAs). This paper focuses on the problem of inexact graph matching which is NP-hard and requires techniques to find an approximate acceptable solution. This problem arises when a nonbijective correspondence is searched between two graphs. A typical instance of this problem corresponds to the case where graphs are used for structural pattern recognition in images. EDA algorithms are well suited for this type of problems.

This paper proposes to use EDA algorithms as a new approach for inexact graph matching. Also, two adaptations of the EDA approach to problems with constraints are described as two techniques to control the generation of individuals, and the performance of EDAs for inexact graph matching is compared with the one of GAs.     

Learning handwriting by evolution: a conceptual framework for performance evaluation and tuning

In this paper we propose a method for evaluating the performance of an evolutionary learning system aimed at producing the optimal set of prototypes to be used by a handwriting recognition system. The trade-off between generalization and specialization embedded into any learning process is managed by iteratively estimating both consistency and completeness of the prototypes, and by using such an estimate for tuning the learning parameters in order to achieve the best performance with the smallest set of prototypes. Such estimation is based on a characterization of the behavior of the learning system, and is accomplished by means of three performance indices. Both the characterization and the indices do not depend on either the system implementation or the application, and therefore allow for a truly black-box approach to the performance evaluation of any evolutionary learning system.     

基于LabVIEW的磁浮车气隙传感器测试系统设计

磁浮车气隙检测常采用电涡流传感器,其在0 ～20 mm量程范围内非线性严重,在实际工作时需要进行非线性校正,同时传感器工作环境恶劣,属于易损器件,需要经常检测维护.设计了一种气隙传感器测试系统,该系统以现场可编程门阵列(FPGA)作为控制核心,使用LabVIEW编写人机界面,实现了磁浮车气隙传感器输出特性的快速测试与在线校正.系统具有硬件结构简单、人机交互友好等特点.实验结果表明:该系统可方便监测传感器内部原始特性,经该系统校正后的传感器输出线性度良好,能满足磁浮列车悬浮控制系统要求.     

磁悬浮列车脉宽编码式绝对定位系统

介绍了一种脉宽编码感应式绝对定位系统在磁悬浮列车中的应用。主要讨论了系统的构成和定位原理。在以上分析的基础上,结合我国磁悬浮列车的发展情况,提出了适合现阶段列车定位的方案,并介绍了相关试验结果。     

基于交叉感应回线的磁悬浮列车测速定位系统

分析了交叉感应回线在磁悬浮列车的测速定位系统中的应用 ,对感应回线的激磁磁场进行了分析 ,介绍了相关的电路的具体实现 ,给出了实验结果。     

基于排列组合熵的高速列车走行部故障分析

针对列车走行部的多种故障模式,提出了基于排列组合熵的高速列车走行部故障诊断方法.对原车和抗蛇行减振器失效、空气弹簧失气、横向减振器失效四种工况进行仿真实验,得到列车不同位置的振动信号.计算列车振动信号的排列组合熵,首先实现了同一速度下不同故障的分离,然后以排列组合熵作为故障特征向量,对特征向量进行多级SVM分类识别.实验结果表明,该方法可以有效识别列车同一速度下的不同故障,高速时对四种工况的平均识别率达97％以上.     

A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms

Natural resource allocation is a complex problem that entails difficulties related to the nature of real world problems and to the constraints related to the socio-economical aspects of the problem. In more detail, as the resource becomes scarce relations of trust or communication channels that may exist between the users of a resource become unreliable and should be ignored. In this sense, it is argued that in multi-agent natural resource allocation settings agents are not considered to observe or communicate with each other. The aim of this paper is to study multi-agent learning within this constrained framework. Two novel learning methods are introduced that operate in conjunction with any decentralized multi-agent learning algorithm to provide efficient resource allocations. The proposed methods were applied on a multi-agent simulation model that replicates a natural resource allocation procedure, and extensive experiments were conducted using popular decentralized multi-agent learning algorithms. Experimental results employed statistical figures of merit for assessing the performance of the algorithms with respect to the preservation of the resource and to the utilities of the users. It was revealed that the proposed learning methods improved the performance of all policies under study and provided allocation schemes that both preserved the resource and ensured the survival of the agents, simultaneously. It is thus demonstrated that the proposed learning methods are a substantial improvement, when compared to the direct application of typical learning algorithms to natural resource sharing, and are a viable means of achieving efficient resource allocations.     

Use of genetic algorithms in training diagnostic rules for process fault diagnosis

The self learning of diagnostic rules can ease knowledge-acquisition effort, and it is more desirable in cases where experience about certain faults is not available. Applications of genetic algorithms to the self learning of diagnostic rules for a pilot-scale mixing process and a continuous stirred-tank reactor system are described in the paper. In this method, a set of training data, which could be obtained from simulations and/or from the recorded data of the previous operations of the real process, is required. The training data is divided into various groups corresponding to various faults and the normal operating condition. Corresponding to each fault, there is a group of initial rules which are coded into binary strings. These rules are evaluated by a genetic algorithm which contains the three basic operators, reproduction, crossover and mutation, and an added operator which preserves the best rule ever discovered. Through this biological-type evaluation, new fitted rules are discovered. The results demonstrate that diagnostic rules fitted with a given set of training data can be efficiently discovered through genetic learning, and, hence, that genetic algorithms provide a means for the automatic creation of rules from a set of training data. It is also demonstrated that bad training data and the inappropriate formulation of rules could degrade the performance of the learning system.     

Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy

Support vector machines (SVMs) are the effective machine-learning methods based on the structural risk minimization (SRM) principle, which is an approach to minimize the upper bound risk functional related to the generalization performance. The parameter selection is an important factor that impacts the performance of SVMs. Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) is an evolutionary optimization strategy, which is used to optimize the parameters of SVMs in this paper. Compared with the traditional SVMs, the optimal SVMs using CMA-ES have more accuracy in predicting the Lorenz signal. The industry case illustrates that the proposed method is very successfully in forecasting the short-term fault of large machinery.     

Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics

The ability to solve inventive problems is at the core of the innovation process; however, the standard procedure to deal with them is to utilize random trial and error, despite the existence of several theories and methods. TRIZ and evolutionary algorithms (EA) have shown results that support the idea that inventiveness can be understood and developed systematically.This article presents a strategy based on dialectical negation in which both approaches converge, creating a new conceptual framework for enhancing computer-aided problem solving. Two basic ideas presented are the inversion of the traditional EA selection (“survival of the fittest”), and the incorporation of new dialectical negation operators in evolutionary algorithms based on TRIZ principles. Two case studies are the starting point to discuss what kind of results can be expected using this “Dialectical Negation Algorithm” (DNA).