基于先验知识与记忆信息的航迹关联聚类算法

为提高目标密集环境下航迹关联的正确率,提出了基于先验知识与记忆信息的航迹关联聚类算法.对来自不同传感器的航迹进行自动聚类,并根据单个传感器中不同航迹代表不同目标这一先验知识,对目标数目过多的聚类进行处理.另外,算法考虑了航迹关联中的记忆信息,提高了航迹关联的准确性,有效地解决了目标密集环境下的误关联问题.仿真结果表明了该算法的有效性.     

被动传感器航迹起始算法

针对目标数目未知和被动传感器测量精度不同情况下的航迹起始问题,提出一种基于多站被动传感器系统的航迹起始算法.该算法首先利用多规则法对测量数据进行预处理,消除一部分“鬼点”,然后将模糊集的思想引入Hough变换,利用模糊函数的隶属度作为参数空间累积量,最后通过序列Hough变换检测航迹.仿真实验表明:该算法可有效改善航迹簇拥现象,抑制虚假航迹的产生,适合于杂波环境下目标的航迹起始.     

一种基于模糊数学的灰色航迹关联方法

在多传感器信息融合系统中,针对传统航迹关联方法在目标密集且航迹交叉的场合下关联效果变差的问题,提出了一种基于模糊数学的灰色航迹关联方法。以两条航迹间的统计距离为基础得到隶属度关系,引入模糊权重因子,简化隶属度计算;将生成的隶属度矩阵输入系统进行航迹间的灰色关联度计算;求得航迹间的关联度,获得各传感器航迹间的关联结果。仿真结果表明,在目标密集且航迹交叉的场合下,该算法的航迹关联性能明显优于加权法、修正K近邻法、模糊数学法,其正确关联概率相较于以上3种方法分别提升了大约27%、13%、3%。     

利用回波幅度信息辅助的模糊聚类关联算法

针对传统模糊聚类关联算法在对近距离运动目标进行跟踪时容易出现航迹合并或航迹丢失的缺点,提出了一种改进的模糊聚类关联算法.该算法综合考虑了目标运动学信息和雷达回波幅度信息对数据关联过程的影响,并在计算模糊隶属度函数时利用雷达回波幅度信息对其进行了修正.该算法在没有显著增加计算量的前提下,提高了对距离较近且运动模式也相似的目标的关联效果.仿真实验结果表明,该算法关联精度较高,计算量适中,具有较好的工程应用价值.     

基于改进加权航迹关联的异步融合算法与仿真

在分布式多传感器信息融合系统中,来自各传感器的局部航迹往往是不同步的。针对分布式多传感器异步航迹关联与融合问题,文中提出一种基于改进加权航迹关联的异步航迹顺序融合算法。把多传感器异步航迹外推校准到同一时刻,实现异步航迹的同步化,再用改进的加权航迹关联算法进行航迹关联,并利用顺序融合算法对已关联航迹进行融合。仿真结果表明了该算法的有效性。     

一种基于航迹隶属度的动态加权融合算法

提出一种适用于传感器精度差异较大时的目标精确跟踪的加权融合算法,该算法通过求解局部航迹之间的最佳隶属度矩阵,将局部航迹本身的最佳隶属度动态地赋给对应传感器权值,实现权值的动态分配。蒙特卡洛仿真表明,精度差异较大传感器组成的融合跟踪网络中,该算法不论应用于匀速目标还是机动目标的精确跟踪,均具有良好的性能。     

分布式多传感信息融合系统的异步航迹关联方法

在分布式多传感信息融合系统中,由于每个局部传感器的采样频率不同以及具有不同的通信延迟,导致来自不同传感器的局部航迹往往是异步的.针对此问题,提出了一种异步航迹关联方法.该方法首先基于最小二乘法实现单传感器的时域融合,从而将多传感异步航迹同步化.接下来,将多传感多目标航迹关联问题转化为在网络中搜索总费用最小的多个互不相交的路径问题,从而获得相应于每个目标的各个传感器的局部航迹组合.仿真试验表明,算法可以有效地解决异步航迹的关联问题,且具有较高的关联成功率.     

基于统计双门限的异类传感器的航迹关联算法

异类传感器由于探测维度以及探测空间的不同,导致传感器之间的航迹关联变得困难。为实现海陆空之间统一的目标信息态势,本文针对雷达、被动声纳的航迹关联问题,提出了一种基于统计双门限的异类传感器航迹关联算法。该方法首先利用假设检验,将N个探测周期内的每个观测样本与第一门限λ进行比较,统计超过门限的样本数,并将统计结果与第二门限L进行比较,从而判断异类传感器之间的航迹是否关联。仿真实验结果表明,该方法在目标航迹交叉、分岔的情况下,仍然有很高的关联正确率,并且对于水下目标的识别也具有重大的意义。     

基于方位与时延信息融合的被动声纳航迹关联

在探测区域存在多个目标的情况下,水声岸站被动测向定位系统必须首先解决多目标的航迹关联问题,即确定各个传感器所测量的信息哪些是来源于同一目标的.通过融合被动声纳所测量到的方位和时延信息提出了一种方位最近邻关联方法,对该方法和传统的多维分配算法进行仿真表明该方法能获得很高的正确关联概率,同时该方法克服了传统的多维分配算法在传感器和目标数量较多时计算量过大的问题,是一种优于多维分配算法的水声被动多传感器多目标航迹关联方法,这说明利用信息融合手段解决水声领域航迹关联问题具有潜在的优势.     

基于新型AFCM的多传感器目标跟踪航迹融合

多目标跟踪是多传感器系统信息融合中的核心技术之一.采用新型的AFCM模糊算法实现对多目标交叉状态下航迹数据关联.该算法定义了一种新的度量空间中的距离,通过新的距离定义有效抑制含有噪声点的样本及目标航迹交叉在迭代中对数据关联聚类中心点的大幅偏差.同时应用改进带加权的航迹融合算法对红外和毫米波雷达传感器测量的航迹数据进行融合.仿真试验证明,新的算法在综合多传感器探测优势的基础上,对航迹的融合结果优于SF算法.新的数据关联算法和改进的加权航迹融合算法为多源信息融合提供了一种可靠有效的多目标跟踪技术.     

Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy

Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A genetic algorithm (GA)-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy-supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. The evaluation by the fuzzy supports of large 1-itemsets is much faster than that when considering all itemsets or interesting association rules. It can also help divide-and-conquer the derivation process of the membership functions for different items. The proposed GA framework, thus, maintains multiple populations, each for one item's membership functions. The final best sets of membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experiments are conducted to analyze different fitness functions and set different fitness functions and setting different supports and confidences. Experiments are also conducted to compare the proposed algorithm, the one with uniform fuzzy partition, and the existing one without divide-and-conquer, with results validating the performance of the proposed algorithm.     

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.     

模糊关联规则算法在入侵检测中的应用

提出了一种结合Apriori和Kuok's算法的改进的模糊关联规则算法.在定义隶属函数、决策树结构和规则集相似度的基础上,采用改进的挖掘算法挖掘数值属性的关联规则.实验结果表明,算法在规则生成和时间效率方面都显示了良好的性能.     

一种自适应的模糊关联规则挖掘算法

关联规则是数据挖掘的重要研究内容之一。传统的关联规则挖掘算法仅适于处理二元属性与分类属性。为更好地处理数量属性,提出了一种自适应的基于模糊概念的量化关联规则挖掘算法。该算法克服了传统的离散分区法的不足,改进了已有模糊关联规则支持度的计算方法。引入了一种基于聚类的隶属函数自动生成方法,使得模糊关联规则的发现不依赖于人类专家给出的隶属函数,使得关联规则的表示自然、简明,有利于专家理解。实验表明该算法是有效的。     

An improved approach to find membership functions and multiple minimum supports in fuzzy data mining

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item’s minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.     

模糊关联规则挖掘模型及其在分解炉中的应用

研究了模糊关联规则挖掘模型在分解炉中的应用,并阐述了模糊聚类算法和关联规则的相关内容,提出运用模糊聚类KFCM算法对实际数据进行计算,得到数据归属于不同类别的隶属度.同时,利用规则挖掘算法MFAR对模糊化的参数进行处理,得到了有价值的模糊规则,解决了实际中专家经验获取的瓶颈问题.试验表明,该方法为水泥生产环节中分解炉的温度控制提供了理论依据和生产优化指导.     

基于模糊支持向量机的多分类算法研究

针对支持向量机理论中的多分类问题以及SVM对噪声数据的敏感性问题,提出了一种基于二叉树的模糊支持向量机多分类算法。该算法是在基于二叉树的支持向量机多分类算法的基础上引入模糊隶属度函数,根据每个样本数据对分类结果的不同影响,通过基于KNN的模糊隶属度的度量方法计算出相应的值,由此得到不同的惩罚值,这样在构造分类超平面时,就可以忽略对分类结果不重要的数据。通过实验证明,该算法有较好的抗干扰能力和分类效果。     

不确定性数据中基于GSO优化MF的模糊关联规则挖掘方法

针对不确定性数据中模糊关联规则的挖掘问题,提出一种基于群搜索优化(GSO)算法优化隶属度函数(MF)的模糊关联规则挖掘方法。首先,将不确定性数据通过三元语言表示模型进行表示;然后,给定一个初始MF,并以最大化模糊项集支持度和语义可解释性作为适应度函数,通过GSO算法的优化学习获得最佳MF;最后,根据获得的最佳MF,利用改进型的FFP-growth算法来从不确定数据中挖掘模糊关联规则。实验结果表明,该方法能够根据数据集自适应优化MF,以此实现从不确定数据中有效地挖掘关联规则。     

二型模糊系统的规则提取算法

模糊规则提取是建立二型模糊系统需要解决的关键问题.提出一种改进的基于c均值模糊聚类算法(FCM)的二型模糊规则提取方法.该方法借助于二型模糊集主隶属度函数的期望与次隶属度函数值之间的联系,能克服已有算法忽略二型模糊集次隶属度函数对模糊聚类结果的影响.仿真实例表明.该算法能成功地提取二型模糊规则,比FCMV算法具有更好的性能和收敛性.     

Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.