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浏览/检索结果: 共125条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model 期刊论文  OAI收割 DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS, 2023, 卷号: 202, 页码: 14 作者:  Zhao, Yizhi;  Qi, Jifeng;  Zhu, Shanliang;  Jia, Wentao;  Gong, Xiang   |  收藏  |  浏览/下载：18/0  |  提交时间：2024/04/07 Barrier layer thickness  Particle swarm optimization  Artificial neural networks  Hybrid models   Dynamic Parameter Optimization of High-Speed Pantograph Based on Swarm Intelligence and Machine Learning 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF APPLIED MECHANICS, 2023, 页码: 19 作者:  Zhou R(周睿);  Xu XH(许向红);  Xu XH(许向红)   |  收藏  |  浏览/下载：8/0  |  提交时间：2023/10/23 Contact force of pantograph-catenary system  selective crow search algorithm  surrogate model  multi-parameter optimization   Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete 期刊论文  OAI收割 MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, 2023, 卷号: 30, 期号: 11, 页码: 2185 作者:  Mei, Xiancheng;  Li, Chuanqi;  Sheng, Qian;  Cui, Zhen;  Zhou, Jian   |  收藏  |  浏览/下载：11/0  |  提交时间：2023/08/02 Rubber-sand concrete  uniaxial compressive strength  back propagation neural network  swarm intelligence optimization algorithm   Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China-Mongolia-Russia Cross-Border Area 期刊论文  OAI收割 REMOTE SENSING, 2023, 卷号: 15, 期号: 1, 页码: 15 作者:  Li, Yuheng;  Xu, Shuxing   |  收藏  |  浏览/下载：15/0  |  提交时间：2023/02/20 ANFIS  wildfire  China-Mongolia-Russia cross-border area  genetic algorithm (GA)  particle swarm optimization (PSO)  random forest   A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 10, 页码: 1918-1932 作者:  Zhuoxuan Li;  Iakov Korovin;  Xinli Shi;  Sergey Gorbachev;  Nadezhda Gorbacheva   |  收藏  |  浏览/下载：9/0  |  提交时间：2023/09/07 Extreme learning machine algorithm with residual correction (RELM), metaheuristic optimization  oil-gas transportation  RIOHTrack  rutting depth   Optimization method of Data interaction in power IoT based on particle swarm algorithm 会议论文  OAI收割 Guangzhou, China, January 14-16, 2022 作者:  Wang TY(王天宇);  Miao, Weiwei;  Zeng, Zeng   |  收藏  |  浏览/下载：74/0  |  提交时间：2022/04/13 cooperative processing  particle swarm algorithm  perceptual device  power IoT   Self-adaptive Bat Algorithm With Genetic Operations 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 7, 页码: 1284-1294 作者:  Jing Bi;  Haitao Yuan;  Jiahui Zhai;  MengChu Zhou;  H. Vincent Poor   |  收藏  |  浏览/下载：19/0  |  提交时间：2022/06/27 Bat algorithm (BA)  genetic algorithm (GA)  hybrid algorithm  learning mechanism  meta-heuristic optimization algorithms   Evolutionary Computation for Expensive Optimization: A Survey 期刊论文  OAI收割 Machine Intelligence Research, 2022, 卷号: 19, 期号: 1, 页码: 3-23 作者:  Jian-Yu Li   |  收藏  |  浏览/下载：12/0  |  提交时间：2022/01/25 Expensive optimization problem  evolutionary computation  evolutionary algorithm  swarm intelligence  particle swarm optimization  differential evolution   Conceptual Design of 3-T all HTS MRI Using No-Insulation Winding Technology: Main Split Coil-System and its Active Shield 期刊论文  OAI收割 IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, 2021, 卷号: 31 作者:  Wang, Tao;  Quan, Hao;  Luo, Rongfu;  Ding, Kaizhong;  Du, Shuangsong   |  收藏  |  浏览/下载：48/0  |  提交时间：2021/11/01 Artificial intelligence  HTS magnet  MRI  no-insulation coil  optimization   A Multi-Layered Gravitational Search Algorithm for Function Optimization and Real-World Problems 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 1, 页码: 94-109 作者:  Yirui Wang;  Shangce Gao;  Mengchu Zhou;  Yang Yu   |  收藏  |  浏览/下载：16/0  |  提交时间：2021/04/09 Artificial intelligence  exploration and exploitation  gravitational search algorithm  hierarchical interaction  hierarchy  machine learning  population structure