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浏览/检索结果: 共4条，第1-4条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Cascading Delays for the High-Speed Rail Network Under Different Emergencies: A Double Layer Network Approach 期刊论文 IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 10, 页码: 2014-2025 作者:  Xingtang Wu;  Mingkun Yang;  Wenbo Lian;  Min Zhou;  Hongwei Wang 收藏  |  浏览/下载：6/0  |  提交时间：2023/09/07 Delay propagation  double layer network  high speed rail network  max-plus algebra   Behavioral feature and correlative detection of multiple types of node in the internet of vehicles 期刊论文 Computers, Materials and Continua, 2020, 卷号: 64, 期号: 2, 页码: 1127-1137 作者:  Xie, Pengshou;  Ma, Guoqiang;  Feng, Tao;  Yan, Yan;  Han, Xueming 收藏  |  浏览/下载：30/0  |  提交时间：2020/11/14 Network layers  Vehicle to vehicle communications  Vehicles  Behavioral features  Communication parameters  Detection accuracy  Detection features  Double layer structure  Energy sufficiency  Message forwarding  Topological conditions   Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer 期刊论文 IET COMPUTER VISION, 2018, 卷号: 12, 期号: 6, 页码: 900-907 作者:  Lian, Jian;  Hou, Sujuan;  Sui, Xiaodan;  Xu, Fangzhou;  Zheng, Yuanjie 收藏  |  浏览/下载：16/0  |  提交时间：2019/12/11 biomedical optical imaging  neural nets  eye  optical tomography  medical image processing  learning (artificial intelligence)  deconvolution  convolution  image restoration  retinal optical coherence  tomography  double convolution layer  image pre-processing tasks  optical coherence tomography systems  degradation effects  current  deblurring research  suitable degradation models  deconvolution  operators  model-based solutions  nonmodel architecture  deep  convolutional neural network  parameter-free situations  deep learning  strategy  traditional model  neural network architectures  retinal OCT  images  state-of-the-art model  OCT deblurring methods   Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China Sang Y.; Fan Y.; Liu B. 收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25 To deal with the defects of BP neural networks used in balance control of inverted pendulum  such as longer train time and converging in partial minimum  this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN)  builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and PureLin function is used in output layer  LM is used in training algorithm. The training data is acquried by three-loop PID algorithm. The model is learned and trained with Matlab calculating software  and the simuLink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision  better astringency and lower calculation. This algorithm has wide appLication on nonLinear control and robust control field in particular. 2011 IEEE.