共查询到17条相似文献,搜索用时 296 毫秒
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综述了近年来国内外在精准农业中农田管理区划分上应用的主要方法,包括K均值聚类算法、模糊C均值聚类算法、加权模糊聚类算法、粒子群优化算法以及改进的蚁群聚类算法概述了各种方法的原理,比较了各种处理方法的优缺,并对分区方法做了简要概括,最后指出了目前研究中存在的问题及今后的研究方向. 相似文献
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提出了一种基于模糊-C均值聚类FCM(Fuzzy C-Means)算法的多模型结构方法,并建立了溶解氧软测量模型.通过对养殖池塘溶解氧软测量模型的研究表明,该方法具有良好的测量精度和鲁棒性,具有良好的在线测量与监控能力. 相似文献
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针对传统的利用极点等密度图和玫瑰图的结构面分组方法主观性强和聚类分析方法不够直观的缺点,建议利用模糊C均值(FCM)聚类的隶属度的结果,结合图形技术绘制隶属度等值线图来进行结构面分组.隶属度等值线图充分利用了模糊C均值聚类中隶属度的信息,展现每个聚类的隶属度的空间分布规律,并且可以分辨出因随机因素形成的结构面,还可以直观地读出聚类中心的范围.三山岛金矿的实例证明,该方法同时具有传统方法直观和聚类分析方法客观的优点,并且能够适应优势组不明显的数据. 相似文献
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对光热电站的出力进行短期预测,可以有效应对太阳能随机性和波动性带来的影响,为电网调度做好准备.该文以青海某光热电站为例,首先使用模糊C均值聚类算法对预处理后的实验数据进行分类,然后通过分析不同聚类类型下出力和气象数据中各因子间的关联程度,充分挖掘出数据间的关系,确定不同类型预测模型的输入变量,进而构建出不同类别下的长短期记忆神经网络预测模型.结果表明,与传统长短期记忆神经网络模型、BP神经网络模型、支持向量机模型和随机森林模型的预测结果相比,基于模糊C均值聚类的长短期记忆神经网络预测模型效果良好,大幅减少了预测误差,验证了该预测模型的有效性. 相似文献
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姚明 《Canadian Metallurgical Quarterly》2011,(5)
本文实现基于颜色的图像检索提出了一种改进的聚类算法,并给出了基于分决主色的检索方法描述图像颜色特征.算法有效地解决了K-均值聚类算法初始聚类中心的选取问题. 相似文献
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一种新的模糊系统建模方法研究与应用 总被引:1,自引:0,他引:1
基于聚类技术和一类模糊神经网络,提出一种新的自动生成模糊系统规则库的设计方法.通过结构辨识(原始数据聚类得到模糊规则数)和参数辨识(RBF网络优化参数)方法,构造模糊系统完善的模糊规则库.通过对丙烯腈收益率问题及函数逼近问题的仿真,说明了该方法具有规则数目少、学习速度快、建模精度高等特点. 相似文献
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《钢铁研究学报(英文版)》2016,(2):116-123
Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes.The fuzzy C-means algorithm was evaluated for rule-base generation for fuzzy and fuzzy grey-box temperature estimation.Experimental data were collected from a real-life mill and three different sets were randomly drawn.The first set was used for rule-generation,the second set was used for training those systems with learning capabilities,while the third one was used for validation.The perform-ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant.The results show that the fuzzy C-means generated rule-bases improve temperature estimation;however,the best results are obtained when fuzzy C-means algorithm,grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%. 相似文献
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JR Mansfield MG Sowa GB Scarth RL Somorjai HH Mantsch 《Canadian Metallurgical Quarterly》1997,21(5):299-308
Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm. 相似文献
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《钢铁研究学报(英文版)》2016,(5):434-441
Grate-kiln-cooler has become a major process of producing iron ore pellets in China. Due to the diversity of the raw materials used and the multi-device multi-variable characteristics,this process still encounters with control problem. An attempt was proposed to deal with this issue. The three-device-integrated feature of the process was firstly analyzed to obtain control strategy,and then an intelligent control system using a combination of expert system approach and Takagi-Sugeno( T-S) fuzzy model was developed. Expert system approach was used to diagnose and remedy the abnormal conditions,while T-S fuzzy model was used to stabilize the thermal state. In the construction of T-S fuzzy rules,antecedents were identified by fuzzy c-mean clustering algorithm incorporated with subtractive clustering algorithm,and consequent parameters were identified by recursive least square algorithm. The control system was applied in a Chinese pelletizing plant and the application results demonstrated its effectiveness of stabilizing the thermal states within three devices. 相似文献
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在模糊ART神经网络的基础上,有机结合模糊模式识别和模糊聚类算法,并通过引入新的学习机制和优化网络结构,建立了改进的新型模糊ART神经网络模型;同时,结合某钢厂连铸现场采集的历史数据,将该模型应用于连铸漏钢预报过程中。其结果表明,该模型对粘结漏钢过程中2种典型温度模式的预报率分别达到95.6%和97.8%,报出率都达到100%,且在避免漏报的同时保证了较低的误报率,能准确识别典型的温度模式和预测拉漏事故的发生。 相似文献
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在模糊ART神经网络的基础上,有机结合模糊模式识别和模糊聚类算法,并通过引入新的学习机制和优化网络结构,建立了改进的新型模糊ART神经网络模型;同时,结合某钢厂连铸现场采集的历史数据,将该模型应用于连铸漏钢预报过程中。其结果表明,该模型对粘结漏钢过程中2种典型温度模式的预报率分别达到956%和978%,报出率都达到100%,且在避免漏报的同时保证了较低的误报率,能准确识别典型的温度模式和预测拉漏事故的发生。 相似文献