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• 自动化研究所 [7]
• 长春光学精密机械与物... [4]
• 沈阳自动化研究所 [2]
• 力学研究所 [1]
• 过程工程研究所 [1]
• 武汉岩土力学研究所 [1]
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• OAI收割 [18]
• iSwitch采集 [1]

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• 期刊论文 [9]
• 会议论文 [6]
• 学位论文 [4]

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• 2024 [1]
• 2022 [1]
• 2020 [2]
• 2019 [2]
• 2014 [1]
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筛选

浏览/检索结果: 共19条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Pantograph-Catenary Interaction Prediction Model Based on SCSA-RBF Network 期刊论文  OAI收割 APPLIED SCIENCES-BASEL, 2024, 卷号: 14, 期号: 1, 页码: 11 作者:  Wu, Mengzhen;  Xu XH(许向红);  Zhang HC(张颢辰);  Zhou R(周睿);  Wang, Jianshan   |  收藏  |  浏览/下载：16/0  |  提交时间：2024/02/19 pantograph-catenary interaction prediction model  pantograph-catenary contact force  SCSA-RBF network   Accurate Quantitative Analysis of Valuable Components in Zinc Leaching Residue Based on XRF and RBF Neural Network 期刊论文  OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 卷号: 42, 期号: 2, 页码: 490-497 作者:  Li Yuan;  Shi Yao;  Li Shao-yuan;  He Ming-xing;  Zhang Chen-mu   |  收藏  |  浏览/下载：39/0  |  提交时间：2022/06/15 XRF  Accurate quantitative analysis  RBF neural network model  Zinc smelting leaching slag   Intelligent modeling and monitoring of micro-droplet profiles in 3D printing 期刊论文  OAI收割 ISA TRANSACTIONS, 2020, 卷号: 105 作者:  Lin, Xin;  Zhu, Kunpeng;  Zhou, Jinxin;  Fuh, Jerry Ying Hsi   |  收藏  |  浏览/下载：20/0  |  提交时间：2020/11/30 Inkjet 3D printing  Droplet profiles  Monitoring  RBF network   Neural-Adaptive Finite-Time Formation Tracking Control of Multiple Nonholonomic Agents with a Time-Varying Target 期刊论文  OAI收割 IEEE Access, 2020, 卷号: 8, 页码: 62943-62953 作者:  Zhou, Kai-Bo;  Wu, Xiao-Kang;  Ge, Ming-Feng;  Liang, Chang-Duo;  Hu, Bing-Liang   |  收藏  |  浏览/下载：26/0  |  提交时间：2020/05/19 Multiple nonholonomic agent system (MNAS)  leader-following formation tracking problem (FTP)  radial basis function (RBF) neural network  distributed controller-estimator algorithm(DCEA)   A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 11, 页码: 3433-3443 作者:  Luo, Lincong;  Peng, Liang;  Wang, Chen;  Hou, Zeng-Guang   |  收藏  |  浏览/下载：40/0  |  提交时间：2020/03/30 Medical treatment  Training  Task analysis  Robot sensing systems  Impedance  Trajectory  Assist as needed (AAN)  challenge level  Gaussian radial basis function (RBF) network  motor capability  rehabilitation robot  upper limb   Landslide spatial modelling using novel bivariate statistical based Naive Bayes, RBF Classifier, and RBF Network machine learning algorithms 期刊论文  OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 卷号: 663, 期号: -, 页码: 43845 作者:  He, Qingfeng;  Shahabi, Himan;  Shirzadi, Ataollah;  Li, Shaojun;  Chen, Wei   |  收藏  |  浏览/下载：11/0  |  提交时间：2020/04/08 Landslide susceptibility  Longhai area  Naive Bayes  RBF Classifier  RBF Network   Robust adaptive neural network control for a class of uncertain nonlinear systems with actuator amplitude and rate saturations 期刊论文  OAI收割 NEUROCOMPUTING, 2014, 卷号: 125, 页码: 72-80 作者:  Yuan, Ruyi;  Tan, Xiangmin;  Fan, Guoliang;  Yi, Jianqiang 收藏  |  浏览/下载：42/0  |  提交时间：2015/08/12 Actuator saturation  RBF neural network  Adaptive control  Robust control   A Recognition Method of Surface Water Based on RBF Neural Network 会议论文  OAI收割 2013 IEEE 4th International Conference on Electronics Information and Emergency Communication, Beijing, China, November 15-17, 2013 作者:  Chen XL(陈雪莲);  Hu JT(胡静涛) 收藏  |  浏览/下载：21/0  |  提交时间：2013/12/26 RBF neutral network  potential outlet  recognition of surface-water   An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection 期刊论文  OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2011, 卷号: 41, 期号: 1, 页码: 107-117 作者:  Xu, Yanwu;  Cao, Xianbin;  Qiao, Hong 收藏  |  浏览/下载：23/0  |  提交时间：2015/08/12 Efficient classification  false-positive rate (FPR)  pedestrian detection  performance evaluation  radial basis function (RBF) neural network.   Study of experimental design and Response Surface method for surrogate model of computational simulation (EI CONFERENCE) 会议论文  OAI收割 2nd Annual Conference on Electrical and Control Engineering, ICECE 2011, September 16, 2011 - September 18, 2011, Yichang, China Xi R.; Jia H.; Xiao Q. 收藏  |  浏览/下载：21/0  |  提交时间：2013/03/25 While the high-precision simulation is widely used in science and technology  Design of Experiment (DOE) based on Response Surface (RS) method can be employed in surrogate model to reduce the cost and error. In order to illustrate the relationship between parameters and response features  several DOE methods and Response Surface (RS) method are studied. The author used polynomial regression and RBF neural network based on orthogonal array to build a rocket aerodynamic discipline surrogate model respectively which proved their feasibility. From the results of the test case  conclusion is drawn that characteristic as well as acclimatization of DOE methods and different approximation should be considered for different issues  so the factors of cost and accuracy could reach a balance synthetically. 2011 IEEE.