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检索条件"主题词=Random Vector Functional Link Networks"
23 条 记 录,以下是21-30 订阅
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Electricity Load Demand Time Series Forecasting with Empirical Mode Decomposition based random vector functional link Network
Electricity Load Demand Time Series Forecasting with Empiric...
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IEEE International Conference on Systems, Man, and Cybernetics (SMC)
作者: Qiu, Xueheng Suganthan, P. N. Amaratunga, Gehan A. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Cambridge Dept Engn Cambridge England
Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and random vector functional link networ... 详细信息
来源: 评论
Particle Size Estimate of Grinding Processes using Robust Regularized RVFLN
Particle Size Estimate of Grinding Processes using Robust Re...
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第28届中国过程控制会议(CPCC 2017)暨纪念中国过程控制会议30周年
作者: Wei Dai Qixin Chen Xiaoping Ma Tianyou Chai School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University
As the production quality index of grinding processes,particle size(PS) is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel random vector functional link networks(RVFLN),namely,rob... 详细信息
来源: 评论
Electricity Load Demand Time Series Forecasting with Empirical Mode Decomposition based random vector functional link Network
Electricity Load Demand Time Series Forecasting with Empiric...
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IEEE International Conference on Systems, Man, and Cybernetics
作者: Xueheng Qiu P. N. Suganthan Gehan A. J. Amaratunga School of Electrical and Electronic Engineering Nanyang Technological Univeristy Singapore Department of Engineering University of Cambridge UK
Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and random vector functional link networ... 详细信息
来源: 评论