一种改进的基于潜在语义索引的文本聚类算法

提出一种改进的基于潜在语义索引的文本聚类算法。算法引入潜在语义索引理论,改进传统的SOM算法。用潜在语义索引理论表示文本特征向量,挖掘文本中词与词之间隐藏的语义结构关系,从而消除词语之间的相关性,实现特征向量的降维。改进传统的SOM算法的局限性,准确给出聚类类别数目的值。实验结果表明,本算法的聚类效果更好,聚类时间更少。     

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map （SOM） with Fisher discriminant analysis （FDA） is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman （TE） process is em- ployed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.     

改进的基于SOM的高维数据可视化算法

通过对高维数据可视化方法的系统研究,提出了一种新的基于自组织映射（Self-Organizing Map,SOM）的算法。为了表现该方法的特点,将其称为三维自组织映射（Three-Dimensional SOM,TDSOM）。它在对高维数据记录集进行SOM分析后将其投影到三维坐标系中的特定的点集上,最终形成三维模型。该模型弥补了传统模型难以清晰准确地展现高维数据的缺陷,并且新模型着重于在一个比二维平面更为广阔的三维立体空间中展现海量数据。使用者通常可以根据当前领域的专业知识在分析模型的基础上得出有意义的模式。新方法可以广泛使用在数据挖掘和模式识别等领域。     

模糊神经网络在机载相机稳像中的应用

针对解决微型飞行器空中拍摄的图像抖动问题,采用自组织递归区间二型模糊神经网络的函数逼近及泛化能力对微型飞行器上的相机振动规律进行模拟,预测机载相机的振动矢量.该自组织递归区间二型模糊神经网络的初始规则数为零,所有规则都是通过结构和参数同时在线学习来产生,网络结构学习采用的是在线区间二型模糊群集,提高自组织递归区间二型模糊神经网络的稳定性及计算精度.仿真结果表明：将自组织递归区间二型模糊神经网络与双BP神经网络进行对比,利用自组织递归区间二型模糊神经网络对微型飞行器相机振动矢量进行预测的精度高.     

一种采用线性递减步长的自组织迁移算法

自组织迁移算法（SOMA）是一种新型的群体智能算法。在对原始自组织迁移算法分析的基础上,针对基于随机变异步长的自组织迁移算法存在的不足,提出了线性递减步长策略,即有针对性地以线性方式动态调整步长,以满足群体迭代在不同阶段的需求,从而加速群体在多峰复杂空间中收敛速度的同时提高算法的局部搜索能力。实验结果表明,该算法优于原始自组织迁移算法和基于随机变异步长的自组织迁移算法。     

地铁车辆电动塞拉门的剩余寿命预测

针对地铁车辆客室电动塞拉门传动装置润滑不良的问题,提出了基于自组织映射（SOM）神经网络、隐马尔可夫链（HMC）模型和蒙特卡罗（MC）仿真的剩余使用寿命预测方法。该方法首先对采集到的电机电流信号进行特征提取;然后利用SOM对提取出的多维特征数据进行融合与编码,将所得结果作为HMC的输入向量,训练得到全部寿命下劣化状态转移矩阵;最后利用MC方法实现对其劣化过程的剩余使用寿命预测。故障模拟实验结果表明,该方法可以在考虑润滑不良故障模式下,有效预测得到电动塞拉门丝杆的剩余使用寿命。     

改进的GHSOM 算法在文本聚类中的应用

信息时代,文本信息极其巨大。本文运用一种改进GHSOM 算法进行文本聚类,该算法具有显著的文本聚类 能力,能够将文本的相似性用多种手段表现。实验结果表明改进GHSOM 算法整体上是优于SOM 算法,它的先进性主要体现 在更短的计算时间,并提供更丰富的有序性表达能力。     

Sensor fusion for online tool condition monitoring in milling

The objective of this paper is to combine a direct sensor (vision) and an indirect sensor (force) to create an intelligent integrated tool condition monitoring (TCM) system for online monitoring of flank wear and breakage in milling, using the complementary strengths of the two types of sensors. For flank wear, images of the tool are captured and processed in-cycle using successive moving-image analysis. Two features of the cutting force, which closely indicate flank wear, are extracted in-process and appropriately pre-processed. A self-organizing map (SOM) network is trained in a batch mode after each cutting pass, using the two features derived from the cutting force, and measured wear values obtained by interpolating the vision-based measurement. The trained SOM network is applied to the succeeding machining pass to estimate the flank wear in-process. The in-cycle and in-process procedures are employed alternatively for the online monitoring of the flank wear. To detect breakage, two features in time domain derived from cutting force are used, and the thresholds for them are determined dynamically. Again, vision is used to verify any breakage identified in-process through the cutting force monitoring. Experimental results show that this sensor fusion scheme is feasible and effective for the implementation of online tool condition monitoring in milling, and is independent of the cutting conditions used.     

基于自组织神经网络的建筑市场执业资格人员信用分类研究

利用自组织神经网络技术,结合建筑市场执业资格人员信用的相关特点,研究了网络中神经元个数的确定、训练步数、网络维数、获胜神经元的领域等对网络结构和执业资格人员信用划分类别的影响,给出了执业资格人员信用分类的网络构造思想和神经网络结构,并以被调查的执业资格人员为例进行了实证研究。研究结果表明,该方法简便、易行,适用于执业资格人员信用分类研究,为开展执业资格人员信用管理奠定了良好的理论方法基础。     

Path Selection in Disaster Response Management Based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters,and has been a key issue in the field of disaster response management.In this paper,we present a path selection algorithm based on Q-learning for disaster response applications.We assume that a rescue team is an agent,which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time.We first propose a path selection model for disaster response management,and deduce that path selection based on our model is a Markov decision process.Then,we introduce Q-learning and design strategies for action selection and to avoid cyclic path.Finally,experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment,which can provide a specific and significant reference for practical management in disaster response applications.