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太阳能热发电系统多模型加权预测控制研究 被引量:1
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作者 丁坤 孙亚璐 +1 位作者 杨昌海 陈博洋 《西北师范大学学报(自然科学版)》 CAS 北大核心 2023年第6期43-49,共7页
针对太阳能热发电过程中广泛存在的随机性和不确定性问题,文中采集实测的数据进行聚类后建立多模型,设计了多模型加权预测控制器.首先,采用模糊聚类算法对实测的太阳能热发电数据进行分类,再用递推最小二乘法建立系统的多模型作为预测模... 针对太阳能热发电过程中广泛存在的随机性和不确定性问题,文中采集实测的数据进行聚类后建立多模型,设计了多模型加权预测控制器.首先,采用模糊聚类算法对实测的太阳能热发电数据进行分类,再用递推最小二乘法建立系统的多模型作为预测模型;其次,取集热器入口温度、太阳辐照强度以及环境温度为扰动信号,以导热熔盐的流速为控制量控制集热器的出口温度,并针对不同的子模型设计预测控制器;第三,按照子控制器的加权策略得到最小的控制增量并对系统进行稳定性分析;最后,进行试验仿真分析,和目前多采用的单模型预测控制结果比较,加权多模型预测控制精度更高,滞后时间更短. 展开更多
关键词 太阳能热发电 多模型预测控制 聚类多模型 线性菲涅尔
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Modeling of the Multi-Target Locating and Tracking in the Field Artillery System 被引量:1
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作者 杨国胜 窦丽华 +1 位作者 陈杰 侯朝桢 《Journal of Beijing Institute of Technology》 EI CAS 2002年第1期14-18,共5页
A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ba... A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system. 展开更多
关键词 field artillery system data fusion closed ball cluster single sensor multi target multi sensor multi target
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Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine 被引量:32
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作者 李修亮 苏宏业 褚健 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2009年第1期95-99,共5页
Presented is a multiple model soft sensing method based on Affinity Propagation (AP), Gaussian process (GP) and Bayesian committee machine (BCM). AP clustering arithmetic is used to cluster training samples acco... Presented is a multiple model soft sensing method based on Affinity Propagation (AP), Gaussian process (GP) and Bayesian committee machine (BCM). AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression (GPR). Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes. 展开更多
关键词 multiple model soft sensor affinity propagation Gaussian process Bayesian committee machine
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