Waterlogging risk assessment based on self-organizing map(SOM)artificial neural networks:a case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.

Comparison of Electric Load Forecasting between Using SOM and MLP Neural Network

Electric load forecasting has been a major area of research in the last decade since the production of accurate short-term forecasts for electricity loads has proven to be a key to success for many of the decision makers in the energy sector, from power generation to operation of the system. The objective of this research is to analyze the capacity of the MLP （multilayer perceptron neural network） versus SOM （self-organizing map neural network） for short-term load forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. On the other hand, the SOM is a type of artificial neural network that is trained using unsupervised data to produce a low-dimensional, discretized representation of an input space of training samples in a cell map. Historical data of real global load demand were used for the research. Both neural models provide good prediction results, but the results obtained with the SOM maps are markedly better Also the main advantage of SOM maps is that they reach good results as a network unsupervised. It is much easier to train and interpret the results.

English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching

The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.

Study of TSP based on self-organizing map

Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is discussed to obtain a faster convergence rate and better solution.Therefore,a new improved self-organizing map(ISOM)algorithm is introduced and applied to four traveling salesman problem instances for experimental simulation,and then the result of ISOM is compared with those of four SOM algorithms:AVL,KL,KG and MSTSP.Using ISOM,the average error of four travelingsalesman problem instances is only 2.895 0%,which is greatly better than the other four algorithms:8.51%(AVL),6.147 5%(KL),6.555%(KG) and 3.420 9%(MSTSP).Finally,ISOM is applied to two practical problems:the Chinese 100 cities-TSP and102 counties-TSP in Shanxi Province,and the two optimal touring routes are provided to the tourists.

基于mRMR-SOM的异步电机轴承故障诊断研究

针对异步电机轴承故障诊断问题,提出了一种融合最大相关最小冗余特征选择算法(mRMR)和自组织映射神经网络(SOM)的故障诊断方法,并将其应用于轴承故障诊断的不同阶段。首先,在实验室环境下搭建了异步电机故障诊断试验平台,在不同电机状态下分别采集振动、电流和电压信号,利用统计学方法获取了高维混合特征集;然后,以互信息为背景,利用mRMR根据特征与状态标签间的相关性和特征间的冗余性,筛选了具备强区分能力的特征,以避免计算冗余和后验诊断性能下降;最后,采用SOM对异步电机健康和轴承故障状态进行了分类识别,验证了SOM对异步电机轴承故障诊断的有效性,以及mRMR对故障诊断结果的影响。研究结果表明:基于mRMR-SOM的异步电机轴承故障诊断方法能够准确地区分健康和故障状态,测试集分类准确率达到89%;使用mRMR特征筛选能够将154维特征降低至17维,缩短23.5%的网络收敛时间,并将分类准确率由89%提升至98%;试验结果验证了基于mRMR-SOM的异步电机轴承故障诊断方法对于异步电机轴承故障诊断问题的有效性,且证实其具备良好的诊断效果。

Algorithm for Solving Traveling Salesman Problem Based on Self-Organizing Mapping Network

Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization prob-lem.Based on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter selection.This paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic algorithms.Simulations show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm accuracy.Compared with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.

基于SOM-FDA利用XRF对药品铝塑包装片的分类

建立了一种对药品铝塑包装片进行快速分类的方法。利用能量色散型X射线荧光光谱(XRF)仪,对47种不同的药品铝塑包装片样品进行了检验,结合自组织映射(self organizing map,SOM)神经网络聚类,通过最大相关性最小冗余(maximum relevance minimum redundancy,MRMR)算法对元素重要性进行排序,并利用最近邻(K-nearest neighbor,KNN)分类器处理样品数据。依据样品中所含元素的种类及质量分数的不同,对药品铝塑包装片进行区分。SOM神经网络聚类的结果为9类,KNN分类器的准确率为97.87%。X射线荧光光谱法操作简便快速、无损检材、灵敏度高。建立的分类模型科学准确,可为公安机关大规模筛选、确定侦查方向、缩短侦查时间提供帮助。

融合SOM神经网络与K-means聚类算法的用户信用画像研究

为提高现阶段基于K-Means聚类算法的用户信用画像模型的准确性和实时性,提出一种融合自组织映射(SOM,Self-Organizing Map)神经网络与K-Means聚类算法的改进方法。通过SOM对用户数据进行降维和特征提取,直接获得最优聚类数目后再用K-Means算法进行聚类分析。通过真实在线借贷平台数据对所提方法进行验证,结果表明,该方法可提升用户信用画像分析的质量,更好地满足金融数据分析中对实时管理和风险控制的要求,为金融机构提供精准的决策支持。

The novel hierarchical clustering approach using selforganizing map with optimum dimension selection

Introduction: Data clustering is an important field of machine learningthat has applicability in wide areas, like, business analysis, manufacturing,energy, healthcare, traveling, and logistics. A variety of clusteringapplications have already been developed. Data clustering approachesbased on self-organizing map (SOM) generally use the map dimensions (ofthe grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters])without any explicit reason for using the particular dimension, andtherefore optimized results are not obtained. These algorithms use somesecondary approaches to map these microclusters into the lowerdimension (actual number of clusters), like, 2, 3, or 4, as the case maybe, based on the optimum number of clusters in the specific data set. Thesecondary approach, observed in most of the works, is not SOM and is analgorithm, like, cut tree or the other.Methods: In this work, the proposed approach will give an idea of how toselect the most optimal higher dimension of SOM for the given data set,and this dimension is again clustered into the lower actual dimension.Primary and secondary, both utilize the SOM to cluster the data anddiscover that the weight matrix of the SOM is very meaningful. Theoptimized two-dimensional configuration of SOM is not the same forevery data set, and this work also tries to discover this configuration.Results: The adjusted randomized index obtained on the Iris, Wine,Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance,Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134,0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, whichoutperforms all other results available on the web and when no reductionof attributes is done in this work.Conclusions: It is found that SOM is superior to or on par with otherclustering approaches, like, k-means or the other, and could be usedsuccessfully to cluster all types of data sets. Ten benchmark data sets fromdiverse domains like medical, biological, and chemical are tested in this work,including the synthetic data sets.

SOM神经网络算法的研究与进展

自组织映射(Self-organizingMaps,SOM)算法是一种无导师学习方法,具有良好的自组织、可视化等特性,已经得到了广泛的应用和研究。该文系统地介绍了SOM算法的产生背景、基本算法。同时对SOM算法的参数设置和其不足进行了分析。重点归纳了其发展过程中的各种改进算法,并对其研究热点及应用领域作了简要的综述,最后展望了该算法的发展方向。

SOM神经网络改进及在遥感图像分类中的应用

针对自组织特征神经网络自身算法的特点和缺陷,采用遗传算法对网络进行改进,形成了基于遗传算法的自组织特征神经网络,并从输入向量、竞争层神经元数量设置和初始权向量设定三方面,结合遥感图像的特性对自组织特征映射网络遥感图像分类的方法进行了改进。将该方法应用于选择西安地区的ETM+卫星遥感图像进行分类实验。结果表明,基于遗传算法的自组织特征映射网络使得遥感图像的分类精度更高,且该算法实现简单,具有一定的工程应用价值。

基于改进的SOM神经网络在水质评价分析中的应用

随着人们对水资源环境的日益重视,各种水质评价方法层出不穷。传统的水质评价方法多采用精确的数学模型进行描述,无法很好的反映水环境中存在的复杂非线性关系,从而影响整体评价结果。因此,本文提出采用一种改进的自组织特征映射神经网络(SOM)方法来进行水质评价,利用SOM神经网络能在无监督、无先验知识的状态下对样本进行自组织、自学习,实现对样本的评价与分类这一特点。通过引入主成分分析,解决SOM神经网络处理高维和相关性强的指标时出现的问题,提高网络收敛速度和聚类准确性。仿真结果表明:改进后的SOM神经网络能够直观准确地评价水体质量,反映水质整体状况。

SOM神经网络和C-均值法在负荷分类中的应用

负荷时变性和分散性已经成为制约负荷模型推广应用的主要因素,而负荷特性分类则是解决这个问题的有效途径。文中提出基于SOM神经网络的C-均值聚类算法的新的负荷分类方法:以负荷模型参数作为负荷动态特性分类特征向量,应用SOM神经网络对初始训练样本进行分类,将获得的聚类数目和各类中心点作为C-均值算法的初始输入进一步聚类。最后通过动模实验的分类结果表明该方法可自动获取分类数,应用于负荷特性分类研究中具有较强的实用性和有效性。

基于核Batch SOM聚类优化的语义Web服务发现机制研究

如何快速、准确和高效地发现满足用户需求的Web服务已成为制约服务发展的瓶颈之一。该文针对现有Web服务发现机制中存在的效率低下和查准率不高的两个主要问题,提出了一个基于核Batch SOM神经网络聚类优化的语义Web服务发现框架。该框架分别在服务表示阶段引入WordNet和隐含语义索引技术对Web服务进行语义扩展和概念语义空间降维;在服务分类阶段利用核机学习理论改进一类适用于Web服务分类的核Batch SOM神经网络算法;在服务匹配阶段提出一种基于核余弦相似性测度的Web服务匹配算法。最后,真实Web服务数据集上的实验结果验证了所提出方法的可行性。

有监督SOM神经网络在入侵检测中的应用

为提高自组织特征映射(SOM)神经网络的分类性能,提出一种有监督SOM神经网络(SSOM)。在输入层和竞争层的基础上增加输出层,根据输入样本的不同预测类别,选取不同的公式调整权值,并训练网络。通过2个权值的组合,实现对样本类别的回归和统计。基于KDD CUP99入侵检测数据集的实验结果表明,与其他SOM网络相比,SSOM具有更好的分类性能和更高的入侵检测率。

基于小波和SOM网络的医学图像融合

提出一种基于小波变换和自组织特征映射(SOM)神经网络的医学图像融合方法,对图像进行小波变换,以图像的小波系数为特征,采用SOM网络对图像进行聚类,并进行模糊分类,从而确定像素融合的权重,得到融合图像。仿真实验结果表明,该方法能够获得良好的性能。

基于SOM的散乱数据点集的B样条曲面重建

利用自组织映射神经网络(SOM)技术对散乱数据点集进行B样条曲面重建时,往往存在网络学习时间过长和学习效果不理想等问题。提出了一种新的神经元初始化方法和分块学习算法,该算法首先运用主元素分析方法(PCA)对散乱数据进行分块,将拓扑结构为四边形的输出层神经元初始化在每块散乱数据的最小二乘平面上进行网络学习和训练,将分块学习得到的各网格曲面拼接成一个整体;然后对该整体网格曲面的边界和内部单独学习,得到一张逼近待重建曲面的双线性B样条曲面;最后对该B样条曲面误差进行了修正。实例证明,该算法可以明显地减少SOM网络学习时间,并改善网络学习效果。