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检索条件"主题词=Evolutionary computation algorithm"
8 条 记 录,以下是1-10 订阅
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Optimization of wind turbine energy and power factor with an evolutionary computation algorithm
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ENERGY 2010年 第3期35卷 1324-1332页
作者: Kusiak, Andrew Zheng, Haiyang Univ Iowa Dept Mech & Ind Engn Seamans Ctr 3131 Iowa City IA 52242 USA
An evolutionary computation approach for optimization of power factor and power output of wind turbines is discussed. Data-mining algorithms capture the relationships among the power output, power factor, and controll... 详细信息
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Multi-objective optimization of HVAC system with an evolutionary computation algorithm
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ENERGY 2011年 第5期36卷 2440-2449页
作者: Kusiak, Andrew Tang, Fan Xu, Guanglin Univ Iowa Dept Mech & Ind Engn Iowa City IA 52242 USA
A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset colle... 详细信息
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Research on rotary crane control using a neural network optimized by an improved bat algorithm
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ARTIFICIAL LIFE AND ROBOTICS 2025年 1-7页
作者: Fujii, Hiroyuki Nakazono, Kunihiko Oshiro, Naoki Kinjo, Hiroshi Univ Ryukyus Grad Sch Engn & Sci Okinawa Japan Univ Ryukyus Fac Engn Okinawa Japan
In this paper, we propose a three-layered neural network controller (NC) optimized using an improved bat algorithm (BA) for a rotary crane system. In our previous study, the simulation results showed that an NC optimi... 详细信息
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Dynamic control of wind turbines
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RENEWABLE ENERGY 2010年 第2期35卷 456-463页
作者: Kusiak, Andrew Li, Wenyan Song, Zhe Univ Iowa Dept Mech & Ind Engn Iowa City IA 52242 USA
The paper presents an intelligent wind turbine control system based on models integrating the following three approaches: data mining, model predictive control, and evolutionary computation. To enhance the control str... 详细信息
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Multi-source, multi-object and multi-domain (M-SOD) electromagnetic interference system optimised by intelligent optimisation approaches
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NATURAL COMPUTING 2020年 第4期19卷 713-732页
作者: Hu, Yihua Li, Minle Liu, Xiangyu Tan, Ying Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China
With the wide use of electromagnetic information equipment, a larg number of wireless radiation systems coexisting in the same region produce intentional or unintentional interference on electronic receivers. For the ... 详细信息
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evolutionary Learning of Differential Morphological Profile Structure for Shape Feature Enabled Faster R-CNN
Evolutionary Learning of Differential Morphological Profile ...
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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on evolutionary computation (IEEE CEC)
作者: Hurt, J. Alex Keller, James Scott, Grant J. Univ Missouri Elect Eng & Comp Sci Columbia MO 65211 USA
Recently, computer vision tasks such as classification and object detection have been dominated by deep neural network (DNN) approaches. As DNN methodologies have matured, researchers have found that some of the most ... 详细信息
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Investigation and Improvement of Distributed Differential Evolution algorithm Cloudde  13
Investigation and Improvement of Distributed Differential Ev...
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13th International Conference on Advanced computational Intelligence (ICACI)
作者: Luo, Liu-Yue Shi, Lin Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China
As a kind of new emerging optimization technology, distributed evolutionary computation (DEC) algorithms have fast developed in recent years. The DEC algorithms, which make use of multiple computers or resources to en... 详细信息
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Adaptive Optimization in evolutionary Reinforcement Learning Using evolutionary Mutation Rates
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IEEE ACCESS 2024年 12卷 165384-165394页
作者: Zhao, Y. Ding, Y. Pei, Y. Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan Univ Aizu Comp Sci Div Aizu Wakamatsu Fukushima 9658580 Japan
Deep reinforcement learning (DRL) has achieved notable success in continuous control tasks. However, it faces challenges that limit its applicability to a wider array of tasks, including sparse rewards and limited exp... 详细信息
来源: 评论