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Short-term load forecasting based on fuzzy neural network
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作者 DONG Liang MU Zhichun (Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China) 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 1997年第3期46-48,53,共4页
The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e... The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory. 展开更多
关键词 short-term load forecasting fuzzy control fuzzy neural networks
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Study on Pests Forecasting Using the Method of Neural Network Based on Fuzzy Clustering 被引量:1
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作者 韦艳玲 《Agricultural Science & Technology》 CAS 2009年第4期159-163,共5页
Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests ... Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests forecasting using the method of neural network based on fuzzy clustering was proposed in this experiment. The simulation results demonstrated that the method was simple and practical and could forecast pests fast and accurately, particularly, the method could obtain good results with few samples and samples correlation. 展开更多
关键词 neural network fuzzy clustering PEST forecasting
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Evaluation of hydraulic fracturing of horizontal wells in tight reservoirs based on the deep neural network with physical constraints
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作者 Hong-Yan Qu Jian-Long Zhang +3 位作者 Fu-Jian Zhou Yan Peng Zhe-Jun Pan Xin-Yao Wu 《Petroleum Science》 SCIE EI CAS CSCD 2023年第2期1129-1141,共13页
Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fra... Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs. 展开更多
关键词 Evaluation of fracturing effects Tight reservoirs Physical constraints Deep neural network Horizontal wells Combined neural network
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Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation 被引量:6
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作者 Li Zhisheng Li Junshan +1 位作者 Feng Fan Zhao Xin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第1期119-124,共6页
A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of elect... A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective. 展开更多
关键词 fuzzy clusteringself-organizing neural network effectiveness evaluation
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Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference
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作者 Evans Nyasha Chogumaira Takashi Hiyama 《Energy and Power Engineering》 2011年第1期9-16,共8页
This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-tu... This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes. 展开更多
关键词 ELECTRICITY PRICE forecasting SHORT-TERM Load forecasting ELECTRICITY MARKETS Artificial neural networks fuzzy LOGIC
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Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system
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作者 Pritpal Singh 《Geoscience Frontiers》 SCIE CAS CSCD 2018年第4期1243-1257,共15页
This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling ... This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model. 展开更多
关键词 Indian summer monsoon rainfall(ISMR) fuzzy set ENTROPY Artificial neural network(ANN) forecasting
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Flatness Control Based on Dynamic Effective Matrix for Cold Strip Mills 被引量:24
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作者 LIU Hongmin HE Haitao +1 位作者 SHAN Xiuying JIANG Guangbiao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2009年第2期287-296,共10页
Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the im... Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced: the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method. 展开更多
关键词 cold strip mill flatness control dynamic effective matrix CLUSTER fuzzy neural network
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基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测
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作者 姜建国 杨效岩 毕洪波 《太阳能学报》 EI CAS CSCD 北大核心 2024年第7期462-473,共12页
为提高光伏功率的预测精度,提出一种变分模态分解(VMD)、模糊熵(FE)、卷积神经网络(CNN)和双向长短期记忆神经网络(BiLSTM)的光伏功率组合预测模型。该方法首先采用VMD将原始光伏序列数据分解成多个子序列,从而减少随机波动分量和噪声... 为提高光伏功率的预测精度,提出一种变分模态分解(VMD)、模糊熵(FE)、卷积神经网络(CNN)和双向长短期记忆神经网络(BiLSTM)的光伏功率组合预测模型。该方法首先采用VMD将原始光伏序列数据分解成多个子序列,从而减少随机波动分量和噪声干扰对预测模型的影响,通过FE对每个子序列进行重组,使用一维CNN的局部连接及权值共享提取不同分量的特征,将CNN输出的特征融合并输入到BiLSTM模型中;利用BiLSTM模型建立历史数据之间的时间特征关系,得到光伏发电功率预测结果。与BiLSTM、CNN-BiLSTM、EEMD-CNN-BiLSTM、VMD-CNN-BiLSTM这4种模型进行比较,该文提出的VMD-FE-CNN-BiLSTM模型在光伏发电功率预测中具有较高的精确度和稳定性,满足光伏发电短期预测的要求。 展开更多
关键词 变分模态分解 卷积神经网络 特征提取 模糊熵 光伏发电功率 预测 双向长短期记忆网络
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基于FCM-LSTM的光热发电出力短期预测 被引量:1
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作者 刘振路 郭军红 +2 位作者 李薇 贾宏涛 陈卓 《工程科学学报》 EI CSCD 北大核心 2024年第1期178-186,共9页
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... 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. 展开更多
关键词 光热电站 气象因素 短期出力预测 长短期记忆神经网络 模糊C均值聚类
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基于深度模糊神经网络的太阳总辐射预测研究 被引量:1
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作者 乔楠 蒋波涛 +2 位作者 郑雨 刘燕东 王锦 《太阳能学报》 EI CAS CSCD 北大核心 2024年第2期59-64,共6页
提出一种基于深度模糊神经网络的太阳总辐射预测模型。首先利用Pearson相关系数分析太阳总辐射关键影响因素,其次利用深度学习多隐含层所具有的特征提取优势将模糊神经网络模块重复连接,构建深度模糊神经网络模型,并使用蝗虫优化算法对... 提出一种基于深度模糊神经网络的太阳总辐射预测模型。首先利用Pearson相关系数分析太阳总辐射关键影响因素,其次利用深度学习多隐含层所具有的特征提取优势将模糊神经网络模块重复连接,构建深度模糊神经网络模型,并使用蝗虫优化算法对其中心值和宽度进行优化。利用所提太阳总辐射预测模型对5个气象站点的相关数据进行仿真实验,并对结果进行分析。仿真结果表明:所提预测模型较其他模型具有较高的预测精度,验证了模型的有效性,可满足无辐射监测站点太阳总辐射预测的需要。 展开更多
关键词 太阳能 太阳辐射 预测 深度模糊神经网络 蝗虫优化算法
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基于有效性分析的自组织模糊神经网络建模方法
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作者 王雪峰 李文静 乔俊飞 《控制工程》 CSCD 北大核心 2024年第3期463-469,共7页
提出了一种基于有效性分析的自组织模糊神经网络(self-organizingfuzzyneural network based on effectiveness analysis, SOEFNN)建模方法。首先,提出了一种针对模糊规则的有效性评价指标,利用样本与规则层输出之间的映射关系进行网络... 提出了一种基于有效性分析的自组织模糊神经网络(self-organizingfuzzyneural network based on effectiveness analysis, SOEFNN)建模方法。首先,提出了一种针对模糊规则的有效性评价指标,利用样本与规则层输出之间的映射关系进行网络模型的有效性分析,通过累积触发的方式实现相应模糊规则的增加或删减,使网络模型在能够处理复杂非线性问题的同时降低其冗余性,使模型更为紧凑。采用梯度下降算法对网络模型进行训练。然后,对所提出的SOEFNN模型进行非线性系统仿真实验和污水处理过程中的出水生化需氧量预测建模,并与其他自组织模糊神经网络模型进行对比。仿真结果表明,所提出的SOEFNN模型能够很好地实现结构和参数的自适应调整,并且具有较好的逼近能力。 展开更多
关键词 有效性分析 自组织模糊神经网络 梯度下降算法 网络建模
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计及温度累积效应的智能电网负荷预测算法
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作者 杨小磊 过夏明 +2 位作者 路轶 张大伟 廖晔 《沈阳工业大学学报》 CAS 北大核心 2024年第2期121-126,共6页
针对温度累积效应对负荷变化造成的影响,提出了一种计及温度累积效应的智能电网负荷预测算法。将持续高温对电网负荷的影响计入预测模型中,并利用模块化神经网络保证了对温度累积效应学习的独立性和准确性。由三个子网络构成多模块神经... 针对温度累积效应对负荷变化造成的影响,提出了一种计及温度累积效应的智能电网负荷预测算法。将持续高温对电网负荷的影响计入预测模型中,并利用模块化神经网络保证了对温度累积效应学习的独立性和准确性。由三个子网络构成多模块神经网络的第一层,以温度、时间及负荷特征为输入参数,所得负荷预测的准确度可达98.13%,且误差较修正前降低了28.63%。结果表明,所提算法具有更高的预测准确性和运行效率。 展开更多
关键词 负荷预测 智能电网 温度累积效应 温度修正 神经网络 多模块 温度特征 时间特征 负荷特征
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基于气象因子Fuzzy模糊处理的短期电力负荷预测 被引量:4
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作者 黄亮亮 王勇 +1 位作者 杨恒 陈帅 《计算机应用与软件》 CSCD 北大核心 2014年第2期171-173,共3页
电力短期负荷预测受各种气象因素的影响,这导致短期电力负荷预测准确度不高。使用模糊逻辑处理温度、湿度和风速的三种影响因素,把它们转化为能被BP神经网络输入识别的具体的数据。该网络经过训练后,得到合适的权值。利用该模糊神经网络... 电力短期负荷预测受各种气象因素的影响,这导致短期电力负荷预测准确度不高。使用模糊逻辑处理温度、湿度和风速的三种影响因素,把它们转化为能被BP神经网络输入识别的具体的数据。该网络经过训练后,得到合适的权值。利用该模糊神经网络,测试电力日负荷数据,预测的平均误差约在±1.69%。 展开更多
关键词 短期负荷预测 神经网络 模糊逻辑 BP算法
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利用卷积神经网络提高天气短期气温预报效果
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作者 王莹 杨晓君 +2 位作者 王迪 张庆 张楠 《气象科学》 2024年第4期793-800,共8页
为提高天津地区温度精细化预报的准确性,本文基于欧洲中期天气预报中心综合预报系统ECMWF-IFS、中国气象局全球同化预报系统CMA-GFS模式数据及天津259个区域自动站的逐小时气温实况数据,构建了一种基于U-Net编解码器结构的3D卷积神经网... 为提高天津地区温度精细化预报的准确性,本文基于欧洲中期天气预报中心综合预报系统ECMWF-IFS、中国气象局全球同化预报系统CMA-GFS模式数据及天津259个区域自动站的逐小时气温实况数据,构建了一种基于U-Net编解码器结构的3D卷积神经网络气温预报模型,能实现天津地区24 h内逐小时气温的网格预报。采用二分搜索的方式对模型众多超参数进行调节,通过148组试验训练得到最优模型,测试集误差为1.226℃。采用多种指标对模型进行检验,结果表明,模型的预报误差整体低于原数值模式,特别是对天津市中南部(含中心城区)和东部沿海有较好的订正效果;其温度日变化预报特征更接近实况,能有效改善原数值模式的日变化预报误差,且模型表现出更强的误差稳定性。 展开更多
关键词 精细化温度预报 卷积神经网络 日变化预报特征 订正效果
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Forecasting Damage Mechanics By Deep Learning 被引量:1
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作者 Duyen Le Hien Nguyen Dieu Thi Thanh Do +2 位作者 Jaehong Lee Timon Rabczuk Hung Nguyen-Xuan 《Computers, Materials & Continua》 SCIE EI 2019年第9期951-977,共27页
We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems.The methodologies that are able to work accurately for less computational and resolving attempts are a signif... We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems.The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays.Relied on learning an amount of information from given data,the long short-term memory(LSTM)method and multi-layer neural networks(MNN)method are applied to predict solutions.Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio,single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale.The predicted results by deep learning algorithms are well-agreed with experimental data. 展开更多
关键词 Damage mechanics time series forecasting deep learning long short-term memory multi-layer neural networks hydraulic fracturing
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考虑时滞效应的模糊主动隔振系统的设计与性能
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作者 郑文杰 盛鹰 +3 位作者 贾彬 严熙川 杨振超 李宁 《西南科技大学学报》 CAS 2024年第3期63-70,共8页
为了更好地实现精密仪器的振动控制,提出基于考虑时滞效应的模糊主动控制隔振系统。该系统采用神经网络,根据当前时刻模糊控制器的输出以及平台的输出数据,实时预测下一时刻平台的运动状态,提前施加作动力对平台进行主动控制。仿真实验... 为了更好地实现精密仪器的振动控制,提出基于考虑时滞效应的模糊主动控制隔振系统。该系统采用神经网络,根据当前时刻模糊控制器的输出以及平台的输出数据,实时预测下一时刻平台的运动状态,提前施加作动力对平台进行主动控制。仿真实验表明,神经网络的预测较为准确,可靠度较高且设计方案合理,能有效预测实时信号并且抑制信号的振荡。系统融合了神经网络对时滞效应的补偿作用和模糊控制器对不确定性的处理能力,可实现精密仪器振动的智能化控制。 展开更多
关键词 主动控制隔振系统 时滞效应 神经网络 模糊控制器 精密仪器
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T区块三叠系储层措施差异性研究及参数优化
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作者 董晓娜 何彦君 +1 位作者 杨兴平 孙殿新 《云南化工》 CAS 2024年第8期174-177,共4页
T区块储层非均质性较强,局部微裂缝发育,水淹井矛盾突出,且区域多叠系叠加发育,治理难度大。结合储层物性、生产动态等资料,采用BP神经网络和主成分分析法综合确定了影响措施效果的主控因素,并优化压裂参数。该工艺增加了储层有效改造体... T区块储层非均质性较强,局部微裂缝发育,水淹井矛盾突出,且区域多叠系叠加发育,治理难度大。结合储层物性、生产动态等资料,采用BP神经网络和主成分分析法综合确定了影响措施效果的主控因素,并优化压裂参数。该工艺增加了储层有效改造体积,提高了措施增油量和有效率。 展开更多
关键词 压裂 增产技术 有效改造体积 工艺优化 BP神经网络
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基于最小二乘状态估计和模糊神经的中长期负荷预测
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作者 张飞飞 沈嘉怡 《电气自动化》 2024年第4期56-59,共4页
可靠准确的中长期电力负荷预测可以使电力公司和发电公司更好地分配配电网络,并有助于在可再生能源并网时提高对电力系统的保护稳定性。为此,提出一种基于加权最小二乘状态估计和模糊神经网络的中长期电力负荷预测方案。基于最小二乘状... 可靠准确的中长期电力负荷预测可以使电力公司和发电公司更好地分配配电网络,并有助于在可再生能源并网时提高对电力系统的保护稳定性。为此,提出一种基于加权最小二乘状态估计和模糊神经网络的中长期电力负荷预测方案。基于最小二乘状态估计得到的潮流信息和来自神经网络得到的预测负荷作为模糊神经网络的输入,借由模糊神经网络生成高质量的中长期电力负荷预测结果,最后在IEEE 30总线系统上进行验证和评估。试验结果表明,所提方案可以实现平均绝对百分比误差低于2.55%,比单独的最小二乘状态估计法和模糊神经网络法拥有更低的平均绝对百分比误差。 展开更多
关键词 中长期负荷预测 最小二乘状态估计 模糊神经网络 数据精细化 WLS-FNN负荷预测模型 IEEE 30总线系统
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Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches
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作者 Abdul Aziz Karia Imbarine Bujang Ismail Ahmad 《American Journal of Operations Research》 2013年第2期259-267,共9页
An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches ... An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices. 展开更多
关键词 CRUDE PALM Oil PRICES NEURO fuzzy neural networks Fractionally Integrated forecast
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一种基于FCM的网络意见领袖传播效能评价方法
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作者 潘梅森 李云安 《湖南城市学院学报(自然科学版)》 CAS 2023年第6期62-68,共7页
为了对网络意见领袖传播效能进行定量评价,本文通过构建其评价指标体系,提出了一种基于模糊C-均值聚类的网络意见领袖传播效能评价方法;并根据网络舆情实际,通过对20位网络意见领袖实证研究,将其传播效能S≥0.7定义为特别关注级,0.5≤S&... 为了对网络意见领袖传播效能进行定量评价,本文通过构建其评价指标体系,提出了一种基于模糊C-均值聚类的网络意见领袖传播效能评价方法;并根据网络舆情实际,通过对20位网络意见领袖实证研究,将其传播效能S≥0.7定义为特别关注级,0.5≤S<0.7定义为关注级,S<0.5定义为普通级.研究结果表明,特别关注级的网络意见领袖有5位,关注级的网络意见领袖有8位,普通级的网络意见领袖有7位,分别占网络意见领袖总数的25%、40%、35%;对于特别关注级的网络意见领袖,管理部门应及时关注其动态,对其进行正面引导,让其主动担负起传播社会主义核心价值观、弘扬中华优秀传统文化、传播正能量的重任. 展开更多
关键词 网络意见领袖 模糊C-均值聚类 传播效能 评价
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