元胞自动机模型在航空发动机故障诊断中的应用

航空发动机一般在高温、高压和高速转动的状态下工作，因此很难获取其全生命周期试验数据。针对无完整生命周期数据的小样本集合进行设计，提出一种基于元胞自动机的航空发动机故障诊断方法，该方法在获取发动机故障特征信息之后，利用元胞的扩散机制获取故障模式的分类边界。其优势在于:在给定的数据集前提下,可以利用较少的运行时间来约减给定的规则样本;可以利用积累或迭代的方式来分步获得原给定样本集的一致性子集。同时，算法的可积累性、运算时间可控等特点，使得该算法能连续应用在航空发动机试验样本数据集由小样本持续增加到大样本的过程中。该方法的应用对发动机的故障诊断的研究具有重要的指导意义。     

智能优化算法在聚类分析中的应用

聚类分析是数据挖掘的重要技术,可根据数据间的相似程度,将数据进行分类,现已广泛应用于工程和技术等领域中。元胞蚁群算法是在将元胞自动机的邻居和规则引入传统蚁群算法的基础上,利用元胞在离散元胞空间的演化规律和蚁群寻优特点的新型优化算法。针对聚类分析的特点,利用元胞蚁群算法进行求解,经实验测试和验证,获得了较好的结果。     

基于改进蚁群算法的CA的应用——以土地利用模拟为例

元胞自动机转换规则的获取对模拟至关重要。对蚁群分类规则挖掘算法进行改进,并将该算法挖掘的转换规则作为元胞自动机的转换规则,提供了一种利用元胞自动机进行模拟的方法,以武清区土地利用模拟为例进行实验,表明了方法的有效性。该方法可应用到其他分类规则和转换规则挖掘中,也可应用到元胞自动机模拟的其他研究中。     

基于空间数据挖掘的分区异步元胞自动机模型研究

传统的元胞自动机模型采用统一的转换规则和相同的演化速率进行演化,忽略了地理现象演变的时空差异性：演化规律的空间异质性和演化速率的空间差异性。针对这一问题,提出了基于空间数据挖掘的分区异步元胞自动机模型,采用双约束空间聚类的方法对元胞空间进行分区,用分区转换规则替代统一转换规则可以体现地理现象演化规律的空间差异性;采用标准格网划分的方法求取异步元胞演化速率,用异步演化速率替代同步演化速率可以体现地理现象演化速率的空间差异性。以杭州市土地利用变化为例对基于空间数据挖掘的分区异步元胞自动机模型进行了实证研究,结果表明：与传统的元胞自动机模型相比,基于空间数据挖掘的分区异步元胞自动机模型具有较高的模拟精度,并且适用于较大区域较长时间段地理现象的动态变化模拟。基于空间数据挖掘的分区异步元胞自动机模型是地理元胞自动机研究的新视角,它将地理现象演变的空间异质性和时间差异性引入到地理元胞自动机模型中,使模型对地理过程的模拟更接近实际地理过程。然而,由于有关分区异步的元胞自动机模型还处于尝试性研究阶段,在元胞空间分区方法、双约束空间聚类算法中权重的确定方法、元胞演化速率的获取方法、元胞转换规则的获取方法、模拟精度评估以及分区异步元胞自动机模型在较大区域较长时间的地理现象模拟中的应用等方面有待进一步的研究与探讨。     

遥感分类精度对湿地模拟预测的影响分析

湿地是生态系统中最为重要的一个生态系统,同时也是近些年来遭受人类活动破坏最为严重的生态系统。近些年来,关于湿地的保护也引起了人们的广泛关注。本文以三江保护区的1999年和2007年两期TM遥感影像为基础,结合人工神经网络和元胞自动机模型,通过对不同分类精度的一系列遥感分类影像作模拟预测,比较分析它们的预测精度,最后结果表明:分类精度和模拟预测精度有着正相关的线性关系,当分类精度达到(Kappa系数)0.75以上时,预测的精度(Kappa系数)才可以达到0.69以上。这一结论为利用元胞自动机模拟预测湿地的空间格局演化时分类精度的选择提供了一定的指导。最后,用此元胞自动机模型预测了2015和2023两年的该区域湿地空间格局图,为三江保护政策的实施提供了一定的依据。     

一种基于元胞自动机的无向图剖分优化算法

运用元胞自动机理论,针对无向图剖分优化问题进行了分析和建模,提出了一种元胞自动机模型以及基于该模型的无向图剖分优化算法。在该元胞自动机模型中,元胞对应于无向图中的结点,元胞的邻居对应于邻接结点,元胞空间对应于无向图中的结点集,元胞的状态对应于所在的结点子集。实验及分析表明该算法不仅能找到无向图的近似最优剖分,而且有效地降低了空间复杂度和时间复杂度。     

基于多耦合元胞自动机的加密算法

提出了耦合系数的概念,构造了一个新的耦合元胞自动机模型,并分析了耦合系数对耦合元胞自动机时空演化的影响。针对已有的单耦合元胞自动机加密系统中存在的不足,提出了基于多耦合元胞自动机的加密算法,该算法将多个元胞进行耦合,增强了两个元胞自动机之间的作用,扩大了相互影响的范围,使得误差扩散更为快速。仿真结果表明,该算法具有更为理想的扩散和扰乱特性,可抵抗蛮力攻击和差分分析攻击。     

元胞自动机压缩算法仿真卫星云图

为减小计算复杂度和提高压缩比,提出基于方块编码和可逆元胞自动机结合的二值图像压缩算法来仿真卫星云图简单实用的元胞自动机编码表示方法。并针对卫星云图特性和业务实际,提出搜索和统计云图元胞邻域的状态集合,改进加权有限元胞自动机算法,对卫星云图数据进行压缩。实验表明,两种编码方法都能有效地减小计算复杂度,提高压缩比。峰值信噪比(PSNR)在30 dB左右,RCA-BTC算法压缩率可以达到15,图像重建仿真效果良好。     

耦合双触发元胞自动机的加密技术研究

针对单向触发元胞自动机加密中误差传播的相似性问题,以及双向触发元胞自动机中密钥空间小的问题,提出了耦合双触发元胞自动机的加密技术,通过相互作用、相互影响的元胞自动机系统之间的共同演化,反向迭代完成数据加密,正向演化完成数据解密,达到解决密文相似性问题以及增加密钥的目的.分析结果表明,该算法可以抵抗蛮力攻击和已知明文、已知密文以及差分分析攻击,具有较高的安全性和很强的实用性.     

变值编码下元胞自动机分类模式可视化分析

对元胞自动机规则的分类是元胞自动机研究领域的核心问题,元胞自动机分类模式研究对社会学、生物学、物理学、计算机科学等各个学科的发展都具有重要意义。基于变值体系编码模式,对初等元胞自动机对应的3变元布尔函数建立起二维可视化模型,利用变值编码体系对函数集合进行不同的编码排列,选择两种经典的分类模式进行分类和染色标记。从彩色的编码图示中,可以观察到原有的分类模式在顺序排列下所看不到的内蕴的对称性。     

基于k-最近邻图的小样本KNN分类算法

提出一种基于k-最近邻图的小样本KNN分类算法。通过划分k-最近邻图,形成多个相似度较高的簇,根据簇内已有标记的数据对象来标识同簇中未标记的数据对象,同时剔除原样本集中的噪声数据,从而扩展样本集,利用该新样本集对类标号未知数据对象进行类别标识。采用标准数据集进行测试,结果表明该算法在小样本情况下能够提高KNN的分类精度,减小最近邻阈值k对分类效果的影响。     

一种基于Bagging-SVM的智能传感器集成学习方法

集成多个传感器的智能片上系统( SoC)在物联网得到了广泛的应用.在融合多个传感器数据的分类算法方面,传统的支持向量机( SVM)单分类器不能直接对传感器数据流进行小样本增量学习.针对上述问题,提出一种基于Bagging-SVM的集成增量算法,该算法通过在增量数据中采用Bootstrap方式抽取训练集,构造能够反映新信息变化的集成分类器,然后将新老分类器集成,实现集成增量学习.实验结果表明:该算法相比SVM单分类器能够有效降低分类误差,提高分类准确率,且具有较好的泛化能力,可以满足当下智能传感器系统基于小样本数据流的在线学习需求.     

HDD: a hypercube division-based algorithm for discretisation

Discretisation, as one of the basic data preparation techniques, has played an important role in data mining. This article introduces a new hypercube division-based (HDD) algorithm for supervised discretisation. The algorithm considers the distribution of both class and continuous attributes and the underlying correlation structure in the data set. It tries to find a minimal set of cut points, which divides the continuous attribute space into a finite number of hypercubes, and the objects within each hypercube belong to the same decision class. Finally, tests are performed on seven mix-mode data sets, and the C5.0 algorithm is used to generate classification rules from the discretised data. Compared with the other three well-known discretisation algorithms, the HDD algorithm can generate a better discretisation scheme, which improves the accuracy of classification and reduces the number of classification rules.     

大数据集挖掘的层次二分抽样算法

针对目前关联规则挖掘的数据集不断增大,而很多抽样算法精度不高还要解决一系列NP难问题等情况。在分析利用频繁1项集进行抽样处理的基础上,提出了高精度的基于频繁n项集平均划分的关联规则挖掘算法——EHAC算法。理论和实验都表明,EHAC能够提高数据挖掘精度,在数据平均划分的同时,尽量保证频繁n项集能够平均划分,减少了数据库扫描次数,一定程度上缩减了数据库规模。     

Evaluating Six Candidate Solutions for the Small-Disjunct Problem and Choosing the Best Solution via Meta-Learning

A set of classification rules can be considered as a disjunction of rules, where each rule is a disjunct. A small disjunct is a rule covering a small number of examples. Small disjuncts are a serious problem for effective classification, because the small number of examples satisfying these rules makes their prediction unreliable and error-prone. This paper offers two main contributions to the research on small disjuncts. First, it investigates six candidate solutions (algorithms) for the problem of small disjuncts. Second, it reports the results of a meta-learning experiment, which produced meta-rules predicting which algorithm will tend to perform best for a given data set. The algorithms investigated in this paper belong to different machine learning paradigms and their hybrid combinations, as follows: two versions of a decision-tree (DT) induction algorithm; two versions of a hybrid DT/genetic algorithm (GA) method; one GA; one hybrid DT/instance-based learning (IBL) algorithm. Experiments with 22 data sets evaluated both the predictive accuracy and the simplicity of the discovered rule sets, with the following conclusions. If one wants to maximize predictive accuracy only, then the hybrid DT/IBL seems to be the best choice. On the other hand, if one wants to maximize both predictive accuracy and rule set simplicity -- which is important in the context of data mining -- then a hybrid DT/GA seems to be the best choice.     

Case generation using rough sets with fuzzy representation

We propose a rough-fuzzy hybridization scheme for case generation. Fuzzy set theory is used for linguistic representation of patterns, thereby producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space. The fuzzy membership functions corresponding to the informative regions are stored as cases along with the strength values. Case retrieval is made using a similarity measure based on these membership functions. Unlike the existing case selection methods, the cases here are cluster granules and not sample points. Also, each case involves a reduced number of relevant features. These makes the algorithm suitable for mining data sets, large both in dimension and size, due to its low-time requirement in case generation as well as retrieval. Superiority of the algorithm in terms of classification accuracy and case generation and retrieval times is demonstrated on some real-life data sets.     

基于多群协同人工鱼群算法的分类规则提取算法

在对基本人工鱼群算法原理分析的基础上,提出了一种多群协同人工鱼群算法用于实现对连续空间变量的分类规则提取问题。定义了基于规则支持度与置信度的规则评价函数,构造了人工鱼在规则提取应用中的特定编码及相关概念的计算公式,给出了该算法的具体实现步骤,并用VC++软件编程实现。最后对Iris和Wine数据集进行测试实验,并与单群体鱼群算法及多种群微粒群算法进行比较。仿真结果表明,该算法能够快速提取分类精度较高的分类规则,因此利用该算法解决连续变量分类规则提取的相关问题是可行且有效的。     

An accuracy-oriented self-splitting fuzzy classifier with support vector learning in high-order expanded consequent space

This paper proposes a self-splitting fuzzy classifier with support vector learning in expanded high-order consequent space (SFC-SVHC) for classification accuracy improvement. The SFC-SVHC expands the rule-mapped consequent space of a first-order Takagi-Sugeno (TS)-type fuzzy system by including high-order terms to enhance the rule discrimination capability. A novel structure and parameter learning approach is proposed to construct the SFC-SVHC. For structure learning, a variance-based self-splitting clustering (VSSC) algorithm is used to determine distributions of the fuzzy sets in the input space. There are no rules in the SFC-SVHC initially. The VSSC algorithm generates a new cluster by splitting an existing cluster into two according to a predefined cluster-variance criterion. The SFC-SVHC uses trigonometric functions to expand the rule-mapped first-order consequent space to a higher-dimensional space. For parameter optimization in the expanded rule-mapped consequent space, a support vector machine is employed to endow the SFC-SVHC with high generalization ability. Experimental results on several classification benchmark problems show that the SFC-SVHC achieves good classification results with a small number of rules. Comparisons with different classifiers demonstrate the superiority of the SFC-SVHC in classification accuracy.     

A novel multiple rule sets data classification algorithm based on ant colony algorithm

Ant colony optimization (ACO) algorithms have been successfully applied in data classification, which aim at discovering a list of classification rules. However, due to the essentially random search in ACO algorithms, the lists of classification rules constructed by ACO-based classification algorithms are not fixed and may be distinctly different even using the same training set. Those differences are generally ignored and some beneficial information cannot be dug from the different data sets, which may lower the predictive accuracy. To overcome this shortcoming, this paper proposes a novel classification rule discovery algorithm based on ACO, named AntMiner_mbc, in which a new model of multiple rule sets is presented to produce multiple lists of rules. Multiple base classifiers are built in AntMiner_mbc, and each base classifier is expected to remedy the weakness of other base classifiers, which can improve the predictive accuracy by exploiting the useful information from various base classifiers. A new heuristic function for ACO is also designed in our algorithm, which considers both of the correlation and coverage for the purpose to avoid deceptive high accuracy. The performance of our algorithm is studied experimentally on 19 publicly available data sets and further compared to several state-of-the-art classification approaches. The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.     

模糊层次商空间的快速属性约简算法

针对传统属性约简算法利用等价关系计算过程繁琐,样本集较大时运行时间长的问题,提出一种利用模糊欧氏距离的快速属性约简算法。定义模糊欧氏距离计算属性间距离;应用层次商空间结构构建约简粒层空间;以粒层空间聚类结果作为约简基础,实现样本集属性约简。仿真结果表明,该算法约简速度不受样本集样本数量限制,运算速度较快,能够在不删除样本的情况下实现数据的快速约简,约简后对数据集分类精度影响小,部分数据集分类精度有所提升,为大规模数据集约简提供了新的研究思路。