基于改进的模糊C-Means航迹聚类方法研究

为指导飞行程序的改善和发现管制员的指挥模式,在分析历史飞行航迹特征基础上,应用最小描绘长度(MDL)原理对航迹特征点进行划分,运用融合了遗传算法和模拟退火算法的改进的模糊C-Means算法对特征点进行聚类,通过最长公共子序列(LCS)算法得到航迹相似性矩阵,利用矩阵得到航迹簇,最后形成中心航迹,算例仿真验证了新算法的有效性.     

基于神经网络的BTT导弹逆控制方案设计

对基于神经网络的倾斜转弯(BTT)导弹逆控制进行了研究。用径向基函数(RBF)神经网络结构和最近邻聚类算法,对导弹系统逆动力学系统进行动态模型辨识,以辨识模型为控制器与BTT导弹控制系统串联构成动态伪线性系统;用逆系统法设计了一种用于BTT导弹非线性控制的经典控制与神经网络在线自学习综合控制方案,实现了导弹三通道的线性化控制和输出的渐近无差跟踪。仿真结果表明:该方案可根据设计指标要求实现对BTT导弹的非线性控制,且有较强的鲁棒性。     

基于合成特征参数的雷达信号聚类分析

针对截获的雷达辐射源脉冲数据提出了一种基于特征参数合成的聚类算法。算法利用全脉冲数据的特征参数,通过对每一个特征参数的分选计算出该参数的加权系数。通过加权系数对每一个特征参数的加权得到合成的特征参数,并利用合成参数对脉冲进行聚类。仿真试验表明该算法能够实现对信号的聚类分选,采用编码的方式有利于降低噪声的影响。     

结合邻域聚类分割的高光谱图像异常检测支持向量数据描述方法

支持向量数据描述方法在高光谱图像小异常目标检测中具有较好的检测性能,但是待检异常的几何形状受到约束和背景的选择具有盲目性影响检测效果,且检测需要对整幅图像进行遍历导致计算量大。提出邻域聚类分割和支持向量数据描述相结合的异常检测方法,首先利用邻域聚类方法分割图像,将几何尺寸小的分割块作为潜在异常目标;其次选择与潜在异常的形状和大小相适应的背景窗进行背景像元收集;最后采用SVDD方法从潜在异常中快速且准确地检测出异常目标。对HYMAP图像的实验结果表明,该算法提高了复杂地物背景下异常的检测性能,降低了SVDD用于高光谱图像异常检测的计算量。     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。     

基于模糊聚类分析的步枪作战效能评估

为了评估步枪的战术效能,科学地构建步枪作战效能评估体系,运用模糊聚类分析方法对步枪作战效能进行评估,最后通过算例验证了所建模型是合理、可行的。     

Optimized Fuzzy Clustering Method for Health Monitoring of Shield Tunnels

Since health monitoring of shield tunnels generally employs multiple sensors belonging to different types,a fine analysis on massive monitoring data,as well as further quantitative health grading,is really challenging.An optimized fuzzy clustering analysis method based on the fuzzy equivalence relation is proposed for health monitoring of shield tunnels.Clustering results are auto-generated by using fuzzy similarity-valued map.The results follow the idea of unsupervised classification.Moreover,a convenient new health index HI is proposed for a fast tunnel-health grading.A case study on Nanjing Yangtze River Tunnel is presented to validate this method.Three types of indicators,namely soil pressure,pore water pressure and steel strain,are used to develop the clustering model.The clustering results are verified by analyzing the engineering geological conditions;the validity and the efficacy of the proposed method are also demonstrated.Further,the fuzzy clustering analysis also represents a potential for identifying abnormal monitoring data.This investigation indicates the fuzzy clustering analysis method is capable of characterizing the fuzziness of tunnel health,and beneficial to clarify the tunnel health evaluation uncertainties.     

改进层次聚类法在复合材料敲击检测中的应用

伴随复合材料的迅速发展,其在航空航天方面的应用也日益普遍,但因复合材料的各向异性,在制造过程中会导致某些缺陷的产生。敲击检测作为一种实时原位的无损检测,在复合材料的检测中效果明显。由于敲击检测尚未找到合理的数据处理方法,所以其应用范围受到限制。从解决敲击检测数据处理方法的角度出发,提出将改进层次聚类法应用于敲击检测中,并在应用实例环节对该方法进行了应用,通过实验结果对比,表明改进层次聚类方法可以解决敲击检测数据处理的问题。     

一种基于粗糙K-均值的椭球基函数神经网络

改进了一种椭球基函数神经网络,它与经典椭球单元神经网络的结构不同,而与径向基函数神经网络结构类似,即它有一个隐含层,并且隐层单元采用椭球基函数,区别于RBF网络的高斯函数。本文采用粗糙K-均值方法求取椭球函数的中心,并给出了该方法中确定初始阈值的步骤。这种改进方法不但使对输入空间的划分局部作用,而且划分区域封闭有界。因此,改进的神经网络具有较好的函数逼近能力和模式识别能力。仿真实验验证了该椭球基函数神经网络的正确性和有效性。