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CLUSTERING PROPERTIES OF FUZZY KOHONEN'S SELF-ORGANIZING FEATURE MAPS 被引量:3
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作者 彭磊 胡征 《Journal of Electronics(China)》 1995年第2期124-133,共10页
A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. ... A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. Simulation results show that the new algorithm is superior to original Kohonen’s algorithm in clustering performance and learning rate. 展开更多
关键词 self-organizing feature maps FUZZY sets MEMBERSHIP measure FUZZINESS mea-sure
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The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps
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作者 A.S. Panfilova L.S. Kuravsky 《Journal of Mathematics and System Science》 2013年第7期353-358,共6页
Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor mode... Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven's Progressive Matrices (Advanced Progressive Matrices) - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained. 展开更多
关键词 self-organizing feature maps intelligence testing Kalman filter
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Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection 被引量:5
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作者 Ling Tan Chong Li +1 位作者 Jingming Xia Jun Cao 《Computers, Materials & Continua》 SCIE EI 2019年第7期275-288,共14页
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... 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. 展开更多
关键词 K-means clustering self-organizing feature map neural network network security intrusion detection NSL-KDD data set
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Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning 被引量:3
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作者 CHEN Xiaoguang LIANG Lin +1 位作者 XU Guanghua LIU Dan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2013年第5期1041-1049,共9页
The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddi... The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed. 展开更多
关键词 feature extraction manifold learning self-organize mapping kernel regression local tangent space alignment
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English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching
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作者 Shu Ma 《IJLAI Transactions on Science and Engineering》 2024年第3期1-8,共8页
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 s... 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. 展开更多
关键词 Chinese-English translation model self-organizing mapping neural network Deep feature matching Deep learning
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基于自组织特征映射模型(SOFM)网络的中国自然资源生态安全区划 被引量:1
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作者 邹易 蒙吉军 +3 位作者 吴英迪 魏婵娟 程浩然 马宇翔 《生态学报》 CAS CSCD 北大核心 2024年第1期171-182,共12页
自然资源生态安全是国家安全的重要组成部分,自然资源生态安全区划对保障区域可持续发展提供了重要途径。基于自然资源数据、生态环境数据和相关区划资料,从生态敏感性与生态服务重要性角度构建了自然资源生态安全评价指标体系,进而揭... 自然资源生态安全是国家安全的重要组成部分,自然资源生态安全区划对保障区域可持续发展提供了重要途径。基于自然资源数据、生态环境数据和相关区划资料,从生态敏感性与生态服务重要性角度构建了自然资源生态安全评价指标体系,进而揭示了中国自然资源生态安全的空间格局;通过建立区划的原则和指标,按照一级区主要反映自然资源空间分布格局,二级区主要揭示自然资源生态安全水平的差异,采用SOFM网络制订了中国自然资源生态安全区划方案。结果显示:(1)中国自然资源生态安全水平整体偏低,以中警与重警状态区域为主,安全和较安全状态的区域仅占24.22%,其中低安全等级区多分布于400mm等降水量线以西的干旱、半干旱区,高安全等级区则集中分布于水热资源与生物资源较为丰富的东南部地区;(2)中国自然资源生态安全区划方案包括8个一级区与27个二级区,总结归纳各大区自然资源的特征和威胁生态安全的问题,并针对二级区自然资源生态安全状况提出了对策建议。研究结果可为分区、分类推进全国自然资源可持续利用和国土空间优化提供理论支持与决策依据。 展开更多
关键词 自然资源生态安全 自组织特征映射模型(sofm)网络 区划方案
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基于SOFM与随机森林的大别山区水土保持空间管控分区
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作者 常耀文 杜晨曦 +4 位作者 刘霞 郭家瑜 张春强 黎家作 姚孝友 《农业工程学报》 EI CAS CSCD 北大核心 2024年第20期250-258,共9页
水土保持是国家生态文明建设的重要内容,水土保持空间管控分区是水土流失区域科学治理的前提与关键。然而,目前水土保持管控区域划分研究还未形成成熟的空间划定方法,且以小流域为单元的水土保持空间管控研究较少。为探索水土保持空间... 水土保持是国家生态文明建设的重要内容,水土保持空间管控分区是水土流失区域科学治理的前提与关键。然而,目前水土保持管控区域划分研究还未形成成熟的空间划定方法,且以小流域为单元的水土保持空间管控研究较少。为探索水土保持空间管控分区的方法,落实差别化保护治理措施,该研究利用通用土壤流失方程(universal soil loss equation,USLE)计算研究区潜在土壤侵蚀模数与实际土壤侵蚀模数,并通过随机森林确定了土壤侵蚀的主要影响因子,基于小流域单元的土壤侵蚀及其影响因子利用自组织映射神经网络(self-organizing feature map,SOFM)确定了大别山区的水土保持空间管控分区。结果显示:1)大别山区的平均潜在土壤侵蚀为84 415.7 t/(km^(2)·a),平均实际土壤侵蚀为210.25 t/(km^(2)·a)。小流域的实际土壤侵蚀主要分布于0~300 t/(km^(2)·a),小流域尺度上潜在土壤侵蚀与实际土壤侵蚀空间分布格局基本一致,高值区主要分布于研究区中部与东部海拔较高的山区腹地;2)植被覆盖度、坡度分别为小流域尺度上潜在土壤侵蚀与实际土壤侵蚀的主要影响因子,植被覆盖度、坡度与土壤侵蚀呈显著正相关(P<0.01)。高植被覆盖区主要分布于林地占比较高的大别山区腹地,坡度较大的区域沿大别山山脊线自西向东分布。3)SOFM结果显示,小流域尺度上的大别山水土保持空间管控区域划分为重点预防区、一般预防区与其他区域,其中重点预防区涉及小流域710个,面积15 287.4 km^(2)。一般预防区共890个小流域,面积18 874.4 km^(2)。两个预防区面积共占研究区61.2%。各区域间的实际土壤侵蚀、潜在土壤侵蚀与坡度差异明显,可作为大别山水土保持空间管控各区域的主要划分指标。研究结果为水土保持空间管控分区提供了思路,为分区域进行水土保持空间管控提供了理论支持与决策依据。 展开更多
关键词 土壤侵蚀 大别山区 sofm 随机森林 水土保持空间管控
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基于SOFM方法的安徽省矿产资源开发主体功能区划研究
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作者 李臻 陈义华 +3 位作者 陈从喜 李政 任升莲 任芳语 《合肥工业大学学报(自然科学版)》 CAS 北大核心 2023年第1期111-117,共7页
文章选择安徽省主要的矿产资源分布区,构建矿产资源开发功能区划指标体系,并通过自组织特征映射(self-organizing feature map,SOFM)网络方法对指标数据进行聚类,根据各聚类结果的区域特征,对安徽省矿产资源开发功能区进行研究。结果表... 文章选择安徽省主要的矿产资源分布区,构建矿产资源开发功能区划指标体系,并通过自组织特征映射(self-organizing feature map,SOFM)网络方法对指标数据进行聚类,根据各聚类结果的区域特征,对安徽省矿产资源开发功能区进行研究。结果表明:安徽省矿产资源分布显著集中,矿产资源富集区主要分布在皖江及皖北地区;安徽省整体生态环境较好,研究区内80.77%的县区生态环境适宜进行适度开发;矿产资源较丰富的县区内生态环境适宜开发,而生态环境指数较高的县区矿产资源匮乏,表明安徽省矿产资源开发与生态保护不存在根本冲突。研究结果解释了安徽省矿产资源空间分布规律,可为分区制定差别化管理政策提供理论依据,对安徽省矿产资源可持续发展规划有一定的参考价值。 展开更多
关键词 矿产资源开发 主体功能区划 自组织特征映射(sofm)网络 空间开发格局
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Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps
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作者 Xingfan ZHOU Zengling YANG +1 位作者 Longjian CHEN Lujia HAN 《Frontiers of Agricultural Science and Engineering》 2016年第2期171-180,共10页
Amino acids are the dominant organic components of processed animal proteins,however there has been limited investigation of differences in their composition between various protein sources.Information on these differ... Amino acids are the dominant organic components of processed animal proteins,however there has been limited investigation of differences in their composition between various protein sources.Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods.In this study,self-organizing feature maps(SOFM) were used to visualize amino acid composition of fish meal,and meat and bone meal(MBM) produced from poultry,ruminants and swine.SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency.Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine,lysine and proline.However,the amino acid composition of the three MBMs was quite similar.The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward.SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining. 展开更多
关键词 self-organizing feature maps VISUALIZATION processed animal proteins(PAPs) amino acid
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Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion 被引量:7
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作者 HASI Bagan MA Jianwen LI Qiqing HAN Xiuzhen LIU Zhili 《Science China Earth Sciences》 SCIE EI CAS 2004年第7期651-658,共8页
Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification result... Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town. 展开更多
关键词 classification WAVELET fusion self-organizing NEURAL network feature map (sofm) ASTER data.
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基于自组织映射神经网络的淮河流域生态系统服务簇时空变化特征 被引量:1
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作者 常耀文 吴迪 +3 位作者 李欢 刘霞 王蕴鹏 郭家瑜 《生态学报》 CAS CSCD 北大核心 2024年第11期4544-4557,共14页
生态系统服务簇的识别是区域生态系统服务管理与优化的关键。量化了2000、2010、2020年淮河流域产水量(WY),水源涵养(WC),土壤保持(SC),生境质量(HQ),水质净化(WP),净初级生产力(NPP)和碳储量(CS)7种生态系统服务。并基于自组织映射神... 生态系统服务簇的识别是区域生态系统服务管理与优化的关键。量化了2000、2010、2020年淮河流域产水量(WY),水源涵养(WC),土壤保持(SC),生境质量(HQ),水质净化(WP),净初级生产力(NPP)和碳储量(CS)7种生态系统服务。并基于自组织映射神经网络(SOFM)识别了生态系统服务簇,探讨了生态系统服务簇的时空变化特征。结果表明:(1)2000—2020年,WP,NPP与WC呈上升趋势,WC的增幅最大;CS与HQ呈下降趋势。淮河流域各生态系统服务具有时空异质性,生态系统服务高值区多位于西南部山区与东北部丘陵山地地区。(2)识别了5个生态系统服务簇:核心生态服务簇,WP服务簇,WY服务簇,NPP服务簇与生态过渡服务簇。核心生态服务簇与生态过渡服务簇的面积总体增加,流域西南部山区与东北部丘陵山地地区生态系统服务提升,2000—2020年,WY服务簇与NPP服务簇间的转移面积较大,WY服务簇面积减少达60.09%,NPP服务簇面积显著增加,2020年占整个流域面积的57.02%。研究结果不仅有助于清晰认识淮河流域生态系统服务簇的空间分布格局及动态变化,也为探索淮河流域可持续的生态系统管理与规划决策奠定了基础。 展开更多
关键词 生态系统服务 自组织映射神经网络(sofm) 生态系统服务簇 淮河流域 InVEST模型
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Pattern recognition of messily grown nanowire morphologies applying multi-layer connected self-organized feature maps
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作者 Qing Liu Hejun Li +1 位作者 Yulei Zhang Zhigang Zhao 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2019年第5期946-956,共11页
Multi-layer connected self-organizing feature maps(SOFMs) and the associated learning procedure were proposed to achieve efficient recognition and clustering of messily grown nanowire morphologies. The network is made... Multi-layer connected self-organizing feature maps(SOFMs) and the associated learning procedure were proposed to achieve efficient recognition and clustering of messily grown nanowire morphologies. The network is made up by several paratactic 2-D SOFMs with inter-layer connections. By means of Monte Carlo simulations, virtual morphologies were generated to be the training samples. With the unsupervised inner-layer and inter-layer learning, the neural network can cluster different morphologies of messily grown nanowires and build connections between the morphological microstructure and geometrical features of nanowires within. Then, the as-proposed networks were applied on recognitions and quantitative estimations of the experimental morphologies. Results show that the as-trained SOFMs are able to cluster the morphologies and recognize the average length and quantity of the messily grown nanowires within. The inter-layer connections between winning neurons on each competitive layer have significant influence on the relations between the microstructure of the morphology and physical parameters of the nanowires within. 展开更多
关键词 Artificial neural networks self-organizing feature maps MONTE Carlo simulation Pattern recognition Messily grown NANOWIRE MORPHOLOGIES
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基于SOFM神经网络的茄子图像分割方法 被引量:9
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作者 姚立健 丁为民 +1 位作者 赵三琴 杨玲玲 《南京农业大学学报》 CAS CSCD 北大核心 2008年第3期140-144,共5页
以将茄子图像从复杂的背景中分割出来为目的,在分析茄子图像色差和色相的基础上,选取R-B、G-B和H作为自组织特征映射(SOFM)网络的输入特征向量,利用该网络自组织学习的特征进行聚类。采用信噪比、面积比、分割时间和傅里叶边界描述子等... 以将茄子图像从复杂的背景中分割出来为目的,在分析茄子图像色差和色相的基础上,选取R-B、G-B和H作为自组织特征映射(SOFM)网络的输入特征向量,利用该网络自组织学习的特征进行聚类。采用信噪比、面积比、分割时间和傅里叶边界描述子等指标来评价分割精度。试验证明,基于SOFM神经网络图像分割评价优于单一阈值分割,适合复杂背景的彩色图像分割。 展开更多
关键词 茄子 图像分割 自组织特征映射(sofm)网络 傅里叶描述子
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SOFM神经网络在道路交通事故分类评价中的应用 被引量:6
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作者 李电生 刘凯 赵闯 《中国安全科学学报》 CAS CSCD 2005年第7期88-91,共4页
随着我国道路交通需求的持续增长和交通建设的快速发展,交通环境和条件有了很大改善,但交通事故仍频频发生,且呈不断增多的趋势,安全已成为交通管理当中一个不容忽视的问题。为了减少交通事故发生次数,降低事故损失程度,需要对交通事故... 随着我国道路交通需求的持续增长和交通建设的快速发展,交通环境和条件有了很大改善,但交通事故仍频频发生,且呈不断增多的趋势,安全已成为交通管理当中一个不容忽视的问题。为了减少交通事故发生次数,降低事故损失程度,需要对交通事故进行分类管理,以便针对不同种类和特征的交通事故采取专门的措施。笔者应用SOFM(自组织特征映射)神经网络对不同原因的道路交通事故进行了分类评价,并根据实际数据的计算和分析提出了相应的防护和控制措施。 展开更多
关键词 道路交通事故 sofm神经网络 分类评价 交通环境 交通管理
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一种基于PCA/SOFM混合神经网络的图象压缩算法 被引量:10
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作者 许锋 方弢 +1 位作者 卢建刚 孙优贤 《中国图象图形学报(A辑)》 CSCD 北大核心 2003年第9期1100-1104,共5页
鉴于用神经网络实现图象压缩是一种非常有效的方法,为此提出了一种基于PCA/SOFM混合神经网络的图象压缩编码算法,并对SOFM网络学习参数的优化进行了探讨.实验证明,与PCA+SOFM连续编码算法和基本SOFM算法相比,这种混合编码算法,由于占用... 鉴于用神经网络实现图象压缩是一种非常有效的方法,为此提出了一种基于PCA/SOFM混合神经网络的图象压缩编码算法,并对SOFM网络学习参数的优化进行了探讨.实验证明,与PCA+SOFM连续编码算法和基本SOFM算法相比,这种混合编码算法,由于占用存储空间少,因而降低了码书设计的计算量,并改善了码书的性能. 展开更多
关键词 神经网络 图象压缩 图象编码 图象质量
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基于SOFM神经网络和HMM的动调陀螺仪故障预测方法研究 被引量:7
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作者 尚永爽 许爱强 吴忠德 《机械科学与技术》 CSCD 北大核心 2012年第10期1711-1715,1720,共6页
针对动调陀螺仪性能参数的退化特点,提出了一种自组织特征映射(SOFM)神经网络和隐马尔可夫模型(HMM)相结合的动调陀螺仪故障预测方法。采集动调陀螺仪的振动、温度、随机漂移、电机功率、电源电压和频率等信号作为表征陀螺退化状态的特... 针对动调陀螺仪性能参数的退化特点,提出了一种自组织特征映射(SOFM)神经网络和隐马尔可夫模型(HMM)相结合的动调陀螺仪故障预测方法。采集动调陀螺仪的振动、温度、随机漂移、电机功率、电源电压和频率等信号作为表征陀螺退化状态的特征信息,利用SOFM神经网络实现多源传感器信息融合;利用HMM方法将不易检测到的早期故障信号转变为容易观测到的信息,实现动调陀螺仪的故障预测。实验结果表明:采用SOFM方法对传感信号的信息融合,能够简单、有效地提取陀螺退化状态的特征信息。运用HMM进行训练和测试,说明了该方法在故障预测中的有效性。 展开更多
关键词 故障预测 自组织特征映射 隐马尔可夫模型 动调陀螺仪
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ES-SOFM混合模型及其在水环境评价中的应用 被引量:4
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作者 许世刚 高新陵 《河海大学学报(自然科学版)》 CAS CSCD 北大核心 2002年第5期53-55,共3页
提出一种基于进化策略 (EvolutionaryStrategy ,ES)和自组织特征映射 (Self OrganizingFeatureMap ,SOFM)神经网络的混合模型进行水环境评价的新方法 .把SOFM网络嵌入到ES中 ,根据SOFM网络的运行状态构造ES的适应性函数 ,利用ES的强搜... 提出一种基于进化策略 (EvolutionaryStrategy ,ES)和自组织特征映射 (Self OrganizingFeatureMap ,SOFM)神经网络的混合模型进行水环境评价的新方法 .把SOFM网络嵌入到ES中 ,根据SOFM网络的运行状态构造ES的适应性函数 ,利用ES的强搜索能力克服SOFM网络对初始权向量和样本输入次序敏感的弱点 .在水环境评价中的应用效果表明 。 展开更多
关键词 ES-sofm混合模型 水环境评价 自组织特征映射 适应性 聚类 水质评价
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基于SOFM神经网络的客运一体化枢纽分类 被引量:1
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作者 曹守华 袁振洲 +1 位作者 韩宝明 李得伟 《北京交通大学学报》 EI CAS CSCD 北大核心 2008年第6期47-51,共5页
分析了在大城市进行交通一体化枢纽建设的必要性,建立了反映客运交通枢纽一体化特性的指标体系,提出采用自组织特征映射(Self-Organizing Feature Map,SOFM)神经网络进行城市交通客运一体化枢纽分类的新方法.最后采用广州交通客运一体... 分析了在大城市进行交通一体化枢纽建设的必要性,建立了反映客运交通枢纽一体化特性的指标体系,提出采用自组织特征映射(Self-Organizing Feature Map,SOFM)神经网络进行城市交通客运一体化枢纽分类的新方法.最后采用广州交通客运一体化枢纽为例,通过定量定性结合的方法,对客运一体化枢纽进行了分类. 展开更多
关键词 交通枢纽 客运一体化 自组织特征映射网络 分类
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应用SOFM神经网络对福州市道路交通事故的研究 被引量:2
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作者 岳小泉 丁艺 +1 位作者 黄晓婷 李晓娟 《森林工程》 2006年第4期35-38,共4页
应用SOFM(自组织特征映射)神经网络对福州市不同原因的交通事故进行了分类分析,以便针对不同种类和特征的交通事故采取专门的措施,并根据实际数据分析提出了相应的防范和控制措施。
关键词 交通事故 交通安全 神经网络 自组织特征映射
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基于SOFM和支持向量机回归的短期负荷预测方法 被引量:2
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作者 苑津莎 张英慧 +1 位作者 牛丽仙 刘献伟 《电力科学与工程》 2009年第8期27-29,44,共4页
提出了一种基于自组织特征映射(SOFM)的聚类分析和支持向量机(SVM)的电力系统短期负荷预测方法。该方法首先利用自组织特征映射网络,通过无监督学习策略对训练样本集进行聚类分析,将其分为若干相似子类,再针对每一子类构造一个支持向量... 提出了一种基于自组织特征映射(SOFM)的聚类分析和支持向量机(SVM)的电力系统短期负荷预测方法。该方法首先利用自组织特征映射网络,通过无监督学习策略对训练样本集进行聚类分析,将其分为若干相似子类,再针对每一子类构造一个支持向量机回归模型;使用基于SVM的回归估计算法建立了回归估计函数表达式,给出了基于SOFM和SVM的网络结构;采用河北省某市的实际负荷数据选择样本进行预测。算例表明该方法能够缩短训练时间,提高预测精度。 展开更多
关键词 自组织特征映射 聚类分析 支持向量机 短期负荷预测 核函数
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