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作 者:徐小丽 米军 陈克琼[3] 范程华 XU Xiaoli;MI Jun;CHEN Keqiong;FAN Chenghua(School of Electronic Information and Electrical Engineering, Hefei Normal University, Hefei 230601, China;Energy Service Center, Hefei University of Technology, Hefei 230009, China;School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)
机构地区:[1]合肥师范学院电子信息与电气工程学院,安徽合肥230601 [2]合肥工业大学能源服务中心,安徽合肥230009 [3]合肥工业大学电气与自动化工程学院,安徽合肥230009
出 处:《合肥工业大学学报(自然科学版)》2019年第10期1335-1340,共6页Journal of Hefei University of Technology:Natural Science
基 金:国家自然科学基金资助项目(61375047)
摘 要:文章采用改进的多尺度小波变换方法,获取太阳光斑不同尺度下的各类特征信息,并结合人类认知机理,构建了多类训练样本的不确定信息认知对象的仿真反馈认知智能机制与计算模型。通过太阳光斑的认知智能决策系统以及太阳光斑认知误差的定义,给出了一种待认知光斑不确定认知结果评价规则和相似光斑的变粒度反馈对比决策系统,准确获取了太阳光斑方位坐标以达到实际应用中快速性和准确性的平衡。The improved multi-scale wavelet transform method is applied to getting all kinds of characteristic information of sun spots in different sizes. Combined with human cognition mechanism, a simulated feedback cognitive intelligent mechanism and computing model of uncertain information cognition objects of multiple training samples is constructed. Through the sun spot cognitive intelligent decision-making system and the definition of the sun spot cognitive errors, the evaluation rules of uncertain cognitive outcome of the sun spot and the variable granularity feedback and decision-making system for similar sun spots are given. The azimuth coordinates of the sun spot are obtained accurately, thus achieving the balance between the speed and accuracy of the application.
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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