Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。

A Genetic Approach to Analyze Algorithm Performance Based on the Worst-Case Instances

Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.

Prediction of Lubricant Physicochemical Properties Based on Gaussian Copula Data Expansion

The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.

High-resolution azimuth estimation algorithm based on data fusion method for the vector hydrophone vertical array

To aim at the problem that the horizontal directivity index of the vector hy- drophone vertical array is not higher than that of a vector hydrophone, the high-resolution azimuth estimation algorithm based on the data fusion method was presented. The proposed algorithnl first employs MUSIC algorithm to estimate the azimuth of each divided sub-band signal, and then the estimated azimuths of multiple hydrophones are processed by using the data fusion technique. The high-resolution estimated result is achieved finally by adopting the weighted histogram statistics method. The results of the simulation and sea trials indicated that the proposed algorithm has better azimuth estimation performance than MUSIC algorithm of a single vector hydrophone and the data fusion technique based on the acoustic energy flux method. The better performance is reflected in the aspects of the estimation precision, the probability of correct estimation, the capability to distinguish multi-objects and the inhibition of the noise sub-bands.

Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization

Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.

Data Mining and Neural Network Techniques in Case Based System

This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.

Adaptive Spectral Clustering Ensemble Selection via Resampling and Population-Based Incremental Learning Algorithm

In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.

A non-group parallel frequent pattern mining algorithm based on conditional patterns

Frequent itemset mining serves as the main method of association rule mining.With the limitations in computing space and performance,the association of frequent items in large data mining requires both extensive time and effort,particularly when the datasets become increasingly larger.In the process of associated data mining in a big data environment,the MapReduce programming model is typically used to perform task partitioning and parallel processing,which could improve the execution effciency of the algorithm.However,to ensure that the associated rule is not destroyed during task partitioning and parallel processing,the inner-relationship data must be stored in the computer space.Because inner-relationship data are redundant,storage of these data will significantly increase the space usage in comparison with the original dataset.In this study,we find that the formation of the frequent pattern(FP)mining algorithm depends mainly on the conditional pattern bases.Based on the parallel frequent pattern(PFP)algorithm theory,the grouping model divides frequent items into several groups according to their frequencies.We propose a non-group PFP(NG-PFP)mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks.Moreover,we present the NG-PFP algorithm for task partition and parallel processing,and its performance in the Hadoop cluster environment is analyzed and discussed.Experimental results indicate that the non-group model shows obvious improvement in terms of computational effciency and the space utilization rate.

Modelling of an hydraulic excavator using simplifiedrefined instrumental variable(SRIV)algorithm

Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator＇s arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable （SRIV） identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the.real machine motion control exoeriments.

占空比传输机制下基于协同预测的时变不确定系统递推滤波

以工业互联网为背景,研究占空比传输机制下一类时变不确定系统的滤波问题,结合协同预测方法设计了新颖的递推滤波算法,解决了占空比传输机制下滤波性能降低的问题。首先给出描述占空比传输机制的数学模型,然后提出结合协同预测方法的递推滤波方案,设计基于占空比机制的递推滤波算法,推导了滤波误差协方差矩阵的一个上界,随后分析这个上界的有界性,实现了在稀疏数据情形下提高滤波性能的目的。仿真结果验证了所提算法的高效性和有效性。

基于ARO-MKELM的微电网攻击检测

智能电网的复杂性和开放性使其在信息交换时更易受到网络攻击的威胁。目前大多数检测方法只关注检测攻击的存在性,不能确定受到攻击的分布式电源的具体位置,导致无法快速将被攻击的分布式电源隔离,继而造成严重的损失。提出一种基于人工兔群优化算法优化多核极限学习机的交流微电网虚假数据注入攻击检测方法。在传统极限学习机中引入组合核函数以提升检测模型的学习能力和泛化能力,并采用具有强全局搜索能力的人工兔群优化算法优化多核极限学习机的核函数参数及正则化系数,进一步提升检测模型的检测精度。利用非训练样本内幅值为55和95的阶跃攻击信号进行仿真验证,检测准确率范围分别达到了(93.44~94.64)%和(98.11~99.23)%,与其他检测模型进行对比分析,验证了所提方法的优越性。

基于贝叶斯估计的空间函数型自回归模型及其应用

目的为了研究函数型数据中响应变量的空间相关性,根据现有研究方法,对具有空间依赖性的函数型数据进行研究,并提出其模型的贝叶斯估计方法。方法以典型空间自回归模型为基础,根据函数响应变量的空间依赖性,假设响应变量和解释变量间存在内生关系,生成空间函数型自回归模型,通过主成分分析将模型中函数型部分变为离散型,然后在给定先验情况下计算模型中参数的完全条件后验分布,使用贝叶斯MCMC方法进行估计。结果使用联合Gibbs采样和随机游动的Metropolis-Hastings算法对模型中参数进行估计,通过模拟研究发现:不同参数下模型的函数型系数以及其他参数的估计偏差和均方误差较小,由此验证了贝叶斯估计方法的有效性,同时将空间函数型模型用于重庆市主城区新房平均价格的实证分析,结果表明所提出模型的贝叶斯估计方法是有效的。结论使用贝叶斯估计方法对模型中参数进行估计,在不同情况下函数型解释变量的估计效果一直都比较好,并且随着样本量的增大,其估计效果也越来越好,可以认为使用贝叶斯估计方法对空间函数型自回归模型进行估计是有效且可行的,同时通过实证分析说明重庆市主城区新房平均价格具有空间自相关性,而且会受到二手房挂牌量的影响。

结合载客热点和POI的出租车停车位划定方法

针对出租车随意停靠造成城市交通拥堵甚至交通事故的问题,利用成都实际区域的出租车GPS(Global Position System)数据和爬取的POI(Point of Interest)数据,使用DBSCAN(Density-Based Spatial Clustering of Application with Noise)聚类算法对上下客点进行聚类,得到出租车的载客热点,根据POI的类型划定载客热点区域的类型,对出租车不同时间的出行需求进行分析,进而划分出出租车的固定停车区域。研究结果表明,出租车固定停车区域的设定与出行者的出行需求有关,即将固定停车区域设置在出行者出行需求多的区域,可以满足出行者的不同出行需求。结合出租车载客热点和爬取POI数据划定固定停车区域的方法具有较高的实用性,可为城市交通安全方面提供理论和现实意义。

基于LBSN数据聚类分析的城市POI感知方法

城市POI的分布情况客观反映了一个城市各行各业的发展情况,传统获取POI的测绘手段成本高、更新周期长、时效性差,而基于位置的社交网络(Location-Based Social Network,LBSN)平台的发展为实现城市POI的感知提供了一种新思路。本文提出一种基于LBSN数据聚类分析的城市POI感知方法,首先,对LBSN数据进行预处理,包括清洗重复数据、删除无效数据、数据预分类等,以提高数据的有效性;其次,提出一种改进的DBSCAN算法,对处理后的数据进行聚类分析,从而得到准确度较高的城市各类POI分布情况。实验结果表明,与传统的DBSCAN算法以及K-means算法相比,本文提出的算法有更好的聚类效果,且在聚类指标上有更大的CH指数值和更小的DBI指数值。

天气雷达间一致性评估算法影响因子分析及改进

基于基数据的雷达间一致性评估的基本方法,分析了雷达高差、地形遮挡、噪声、充塞、时空重叠率、衰减等对一致性评估算法的结果质量的影响,并采取了相应的改进措施:使用雷达的波束遮挡比例数据进行遮挡订正或直接剔除存在遮挡的重叠点;对信噪比较低、充塞不充分及存在异常的重叠点予以剔除;增加了时间、空间重叠率因子,分析了不同阈值时间、空间重叠率对评估结果的影响。改进措施对一致性评估结果中的偏差均值、标准偏差的质量均有了一定的提高。使用改进后的算法对岳阳站雷达系统偏差和苏皖地区雷达回波一致性做了评估。结果表明:岳阳站在维护前后与周边雷达的偏差值有明显的降低,并回到正常水平,标准偏差值也略有降低;苏皖地区雷达间偏差均值大部分在2 dB以内,雷达间偏差均值所组成的矢量三角形闭合度较好,标准偏差在2.9~3.3 dB,相关系数在0.40~0.55。

基于隐结构模型和频繁项集的针刺治疗慢性前列腺炎辨证取穴规律

目的:探讨针刺治疗慢性前列腺炎的辨证取穴规律,为临床治疗慢性前列腺炎提供帮助。方法:检索国家知识基础设施数据库(CNKI)、中文科技期刊数据库(CCD)等数据库中关于针刺辨证论治慢性前列腺炎的文献,构建包含症状、证型、穴位处方的慢性前列腺炎病历数据库,运用隐结构模型分析、频繁项集等方法,分析针刺治疗慢性前列腺炎的辨证取穴规律。结果:共纳入文献64篇,涉及穴位91个,症状248项。高频穴位如三阴交、中极等;高频症状包括舌体瘀点瘀斑、苔黄腻、滴白、尿急等;构建隐结构模型,得出慢性前列腺炎主要证型有湿热下注、肾阳不足等;挖掘出症状-穴位频繁项集、症状-证型-穴位频繁项集各4项。症状-穴位频繁项集如“尿急+滴白+阳痿+早泄+肾俞+足三里”,症状-证型-穴位频繁项集如“尿频+尿急+苔黄腻+滴白+舌体瘀点瘀斑+湿热瘀阻+三阴交+会阴”,提示治疗时可根据相应症状判定证型及选择对应穴位。结论:针刺治疗慢性前列腺炎多以三阴交、中极、关元等为主要穴位,穴位配伍依据临床情况辨证选穴,此可为临床治疗慢性前列腺炎提供参考。

基于隐结构模型联合频繁项集的针灸治疗弱精子症不育患者的辨证取穴规律

目的:探讨针灸治疗弱精子症不育患者的辨证取穴规律,为临床治疗弱精子症不育患者提供帮助。方法:检索国家知识基础设施数据库(CNKI)、中国生物医学文献数据库(CBM)、中国学术期刊数据库(CSPD)及中文科技期刊数据库(CCD)等数据库有关针灸治疗弱精子症的相关文献,提取症状、取穴处方信息构建医案数据库,采用隐结构模型、频繁项集的数据挖掘方法,分析针灸治疗弱精子症的辨证取穴规律。结果:纳入文献35篇,涉及症状62种,穴位49个。高频症状包括腰酸、神疲乏力等,高频腧穴包括关元、肾俞等。对症状、腧穴、证型进行频繁项集分析,挖掘出症状-腧穴频繁项集4项,包括耳鸣+头晕+腰酸+关元+肾俞等;证型-症状频繁项集4项,包括肾精亏虚证+健忘+性欲淡漠+神疲乏力+脉细等;证型-症状-腧穴频繁项集4项,包括肾精亏虚证+性欲淡漠+神疲乏力+头发易脱+关元等。结论:针灸治疗弱精子症多以关元、肾俞、足三里为主穴。

高速铁路信号云联锁系统数据容错技术研究

为适应铁路信号系统的数字化发展需求,将云计算技术引入铁路信号联锁系统(云联锁),有助于为联锁系统提供更高的灵活性、可扩展性和计算性能。目前,研究如何确保云联锁系统的安全性问题极具挑战性。为了解决目前商用云计算平台尚不能提供满足铁路信号系统安全苛求等方面的技术保障措施,针对云联锁系统的安全性,提出一种用于云联锁系统的数据容错恢复方案,确保云平台可以运行联锁应用,并保障云联锁功能的安全性和正确性。首先,对既有计算机联锁系统的安全措施和目前针对云平台的安全技术手段进行分析,找出云联锁系统的安全保障措施的难点,提出一种云联锁系统结构,分析进路控制过程,探索适合于针对云联锁的监控方法。然后,根据进路控制过程,使用特征编码方法对联锁进路和输入信息进行编码。运用基于矩阵算法的容错技术,检测联锁运算数据错误,通过监控软件的运行行为来检测云联锁系统潜在的设计缺陷和计算错误等。对多节点的联锁运算故障进行分析,提出针对云联锁可能发生的多重故障的故障恢复方案,并对采取的措施进行详细分析。最后,通过平均危险失效概率定量计算分析云平台的安全性。研究结果表明:与传统技术相比,提出的监控方案具备更高的安全性能。研究结果可为进一步研究类似的高安全铁路信号系统、铁路信号系统数字化和信息化研究提供参考。

基于反向鲸鱼-多隐层极限学习机的电网FDIA检测

针对目前已有的电力信息物理系统虚假数据注入攻击检测方法由于特征表达能力有限,而导致无法精确获取受攻击位置的问题,提出一种基于反向学习鲸鱼优化多隐层极限学习机的虚假数据注入攻击定位检测方法。所提方法不仅将极限学习机拓展为多隐层神经网络,解决其特征表达能力有限的问题,而且引入鲸鱼优化算法对多隐层极限学习机的各隐层神经元个数进行寻优并采用反向学习策略提高其收敛速度和检测精度,以防止随机确定各隐层神经元个数对检测方法的泛化性能和定位检测结果造成影响。通过在不同场景下对IEEE-14和57节点测试系统进行大量实验,验证了所提方法能够通过历史数据自动识别受攻击的系统状态量所对应的精确位置。与其他多种方法相比,所提方法具有更优的精度、召回率和F1值。