Learning Bayesian network structure with immune algorithm

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

Learning Bayesian networks using genetic algorithm

A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

基于覆盖的构造性学习算法SLA及在股票预测中的应用

覆盖算法是神经网络学习算法中的一个十分有效的方法 ,它克服了基于搜索机制的学习方法和规划学习方法计算复杂性高 ,难以用于处理海量数据的不足 ,为神经网络提供一个构造性的学习方法 但该方法是建立在所有训练样本都是精确的假设上的 ,未考虑到所讨论的数据具有不精确的情况 ,若直接将该方法应用于数据不精确情况 ,所得到效果不理想 主要讨论数据具有不精确情况下的时间序列的预测问题 为此将原有的覆盖算法进行改进 ,引入“覆盖强度”和“拒识样本”的概念 ,并结合这些新概念给出相应的覆盖学习算法 (简称SLA) ,最后将SLA算法 ,应用于金融股市的预测 ,具体应用到以上 (海 )证 (券 )综合指数构成的时间序列的预测 ,取得了较好的结果 。

基于“同辈协同学习”的SLA嵌入式教学研究——以FSU《管理会计》课程为例

针对部分学生难以通过部分难度较大的课程而导致辍学和生源流失的问题,费力斯州立大学于1993年开始推行基于"同辈学习"的SLA工作室项目,通过专业教师、学生助教、学生导师形成一个老师辅导、同辈学生之间的学习交流群体,形成稳定的学习伙伴关系,协助GPA2.0以下学生掌握学习方法、加强预习、辅导、答疑、日常测评,建立有效的测评、反馈、分析机制,成功地帮助学生克服学习障碍,提高学习成绩。本文通过SLA项目与一帮一、班级导师制度的对比分析,寻求提高学生成绩的解决方法。

基于Q-learning的轻量化填充结构3D打印路径规划

针对轻量化填充结构模型,提出了一种基于Q-learning算法的3D打印路径规划方法,来改善该结构路径规划中转弯与启停次数较多的问题。首先对填充和分层处理后的模型切片进行预处理,然后以减少打印头转弯和启停动作为目标,构建相对应的马尔可夫决策过程数学模型,多次迭代动作价值函数至其收敛,求解出一组取得最大回报值的动作策略,按照所设定的数学模型将该策略转义输出为打印路径,最后通过对比实验进行验证。实验结果表明:该方法能有效减少打印头的转弯和启停次数,增加打印路径的连续性,节省打印时间,同时可以在一定程度上提升打印质量。

A Multi-Layered Gravitational Search Algorithm for Function Optimization and Real-World Problems

A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve population.Though GSA is an effective population-based algorithm,it exhibits low search performance and premature convergence.To ameliorate these issues,this work proposes a multi-layered GSA called MLGSA.Inspired by the two-layered structure of GSA,four layers consisting of population,iteration-best,personal-best and global-best layers are constructed.Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population.Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low,medium and high dimensions demonstrates that MLGSA is the most competitive one.It is also compared with four particle swarm optimization variants to verify its excellent performance.Moreover,the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance.The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance.Its computational complexity is given to show its efficiency.Finally,it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency.

Causal constraint pruning for exact learning of Bayesian network structure

How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible parent sets,improving state-ofthe-art learning algorithms’efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints,these exact learning algorithms can prune about 70%possible parent sets and reduce about 60%running time while only losing no more than 2%accuracy on average.Additionally,with sufficient samples,exact learning algorithms with causal constraints can also obtain the optimal network.In general,adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.

Fuzzy adaptive learning control network with sigmoid membership function

To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

STUDIES OF THE DYNAMIC BEHAVIORS OF A CLASS OF LEARNING ASSOCIATIVE NEURAL NETWORKS

This paper investigates exponential stability and trajectory bounds of motions of equilibria of a class of associative neural networks under structural variations as learning a new pattern. Some conditions for the possible maximum estimate of the domain of structural exponential stability are determined. The filtering ability of the associative neural networks contaminated by input noises is analyzed. Employing the obtained results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical associative neural network that stores a given set of vectors as the stable equilibrium points as well as learns new patterns can be developed. Some new concepts defined here are expected to be the instruction for further studies of learning associative neural networks.

Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases

The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。

一种用于变压器故障诊断的贝叶斯网络优化方法

针对变压器故障诊断效率低的问题,文中将油中溶解气体分析与人工智能方法相结合,提出了一种改进蝗虫优化算法优化贝叶斯网络的变压器故障诊断方法。利用差分进化算法和与模拟退火算法对蝗虫算法进行改进,提高了算法的优化能力。将改进蝗虫算法应用于贝叶斯网络结构来学习构建变压器故障诊断模型,利用所提方法对变压器进行故障诊断。实验结果表明,该方法诊断正确率达到了92.7%,与其他算法所构建的诊断模型相比具有更高的故障诊断准确率。

基于RegNet-CSAM与ZOA-KELM模型的滚动轴承故障诊断

针对现有深度卷积神经网络对滚动轴承混合故障诊断效果不佳以及模型复杂度过高导致计算成本过大等问题,提出了一种基于RegNet-CSAM与ZOA-KELM模型的滚动轴承故障诊断方法。该模型由RegNet-CSAM网络和ZOA-KELM分类算法组成。首先,将融合了通道和空间特征的注意力机制CSAM与组卷积残差模块结合,提升该结构的表征能力,由此构建的RegNet-CSAM网络,模型复杂度为0.48GF;其次,在分类阶段将斑马优化核极限学习机(ZOA-KELM)替代原来网络中使用的Softmax函数完成最后的分类任务。滚动轴承故障诊断试验结果表明,RegNet网络对滚动轴承混合故障样本容易产生误判,CSAM的融入虽将RegNet网络的分类精度进一步提高,但是仍然存在一定程度的滚动轴承混合故障误判问题;而将ZOA-KELM替代Softmax函数后再对RegNet-CSAM网络输出特征进行分类,能够有效识别出滚动轴承的单一和混合故障,准确率达到了99.92%。所提方法对比其他网络,诊断精度最大提升5.02%,模型复杂度最大缩减32倍。

基于改进萤火虫算法的贝叶斯网络结构学习

贝叶斯网络是目前不确定知识表达和推理领域最有效的理论模型之一,利用贝叶斯网络进行分析和推理前首先需要通过结构学习和参数学习获取其网络模型,其中结构学习是参数学习的基础。针对现有萤火虫算法不符合生物学规则以及学习贝叶斯网络结构存在效率低、容易陷入局部最优等问题,设计了一种基于互信息与性别机制的萤火虫算法(firefly algorithm based on mutual information and gender mechanism,MGM-FA)。首先,通过计算节点互信息得到贝叶斯网络骨架图,基于骨架图驱动MGM-FA算法生成初始种群;其次,引入基于性别机制的个性化贝叶斯网络种群更新策略,以保障贝叶斯网络个体的多样性;最后,引入局部优化器和扰动操作符,增强算法的寻优能力。分别在不同规模的标准网络上进行仿真实验,与现有同类型算法相比,该算法精度和效率均有所提升。

近似图引导的演化贝叶斯网络结构学习算法

贝叶斯网络结构学习是贝叶斯网络推理及应用的基础.搜索高质量的节点序是贝叶斯网络结构学习的一类重要方法.针对在节点序空间中,搜索高质量节点序存在的难以高效、准确评估解的问题,本文提出了一种近似图引导的演化贝叶斯网络结构学习算法.首先,该算法利用互信息构建无向近似图;其次,该算法通过结合节点序和无向近似图构造有向图结构,将其贝叶斯信息准则评分作为节点序的适应度来高效评估节点序,并在演化优化的框架下,使用提出的基于Kendall Tau Distance的交叉算子和基于逆度的变异算子搜索最优节点序;最后,将搜索到的最优节点序输入K2算法得到其对应的贝叶斯网络结构.在4种不同规模网络上的实验结果表明,该算法在收敛时间和准确度之间取得了较好的平衡,其评分相较于对比算法中的次优解分别提升了10.91%、12.28%、53.96%、10.87%.

利用凝聚层次聚类辨识电网中脆弱输电断面

针对传统的输电断面识别主要依靠电网运行调度人员经验划分电网、寻找断面存在不准确问题,采用改进的凝聚层次聚类算法对电网薄弱断面进行辨识;建立基于改进凝聚层次聚类算法的薄弱辨识模型,首先以潮流介数作为线路权重来构建节点相似矩阵,其次通过聚类算法确定电网最优分区数,完成电网分区;根据电网分区结果结合图论,搜索初始输电断面,对所有初始断面搜索结果进行电气连接检验,最终确定脆弱输电断面;通过聚类算法辨识脆弱断面,并应用机器学习方法提高识别脆弱断面的效率。结果表明,基于聚类的脆弱断面辨识方法在识别电网系统中的脆弱输电断面具有可行性,可以有效提升辨识效率。

中空铜纳米线的拉伸断裂分布与初始滑移分布的关系

构建了系列球形中空结构的纳米线(NW),采用分子动力学(MD)对每个模型300个不同初始态的样本开展拉伸形变模拟。并利用基于密度的噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)机器学习算法,获得了初始滑移面的位置。基于大数据统计,分析了初始滑移位置分布以及断裂位置分布两者之间的相关性。研究结果表明:当内部中空半径较小时,断裂位置分布形成于塑性形变阶段,初始滑移分布与断裂位置分布之间无显著的相关性;但是对于脆性特征明显的大中空半径的NW,高能内表面诱导产生的滑移面迅速积累,产生颈缩并导致最终的断裂。因此当内部中空结构达到一定尺寸时初始滑移位置的分布与最终断裂位置的分布之间有明确的因果关系。

基于MMPC-FPSO贝叶斯网络混合结构学习方法

针对贝叶斯网络结构学习的过程中网络结构规模随节点数增加呈指数增长,导致网络结构搜索空间增大,进而导致网络结构学习算法效率低下的问题,提出一种基于最大最小父子集合约束与萤火虫粒子群搜索算法的贝叶斯网络混合结构学习方法。首先,针对粒子群算法在解决贝叶斯网络结构学习过程中,随机初始化网络结构种群导致算法搜索效率低下,网络结构准确性低的问题提出一种基于改进的最大最小父子集合算法的种群约束方法。其次,针对传统的基于粒子群评分搜索方法速度慢,精度低,易陷入局部最优的问题,提出一种基于萤火虫算子的粒子寻优策略。最后,为了验证所提方法的正确性和优越性,将上述方法用于3种标准网络的结构学习。仿真结果表明:所提算法与传统的基于粒子群的结构学习方法相比,所得的贝叶斯信息准则评分与标准网络评分的差距分别缩小了68.7%、65.5%、34.1%。