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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 内容类型：会议论文     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 Reliable Shortest Path Guidance in Stochastic Road Networks Using Convolution-Based Path Finding Algorithm 会议论文 CICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals 作者:  Chen, P.;  Tong, R.;  Lu, G.;  Wang, Y. 收藏  |  浏览/下载：12/0  |  提交时间：2019/12/30 Convolution  Genetic algorithms  Graph theory  Roads and streets  Stochastic systems  Computational workload  Large-scale network  Modified genetic algorithms  On-line applications  Path-finding algorithms  Shortest path searching  Travel time reliability  Travel time variabilities  Travel time   Reliable Shortest Path Guidance in Stochastic Road Networks Using Convolution-Based Path Finding Algorithm 会议论文 18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018, Beijing, China, 2018-07-05 作者:  Chen, Peng;  Tong, Rui;  Lu, Guangquan;  Wang, Yunpeng 收藏  |  浏览/下载：2/0  |  提交时间：2019/12/30 Adaptive detection and correction method for anomalous wind speed 会议论文 19-1#, Jiangong Road, Xi'an, China, October 25, 2016 - October 28, 2016 作者:  Chen, Wei;  Wu, Butuo;  Pei, Xiping;  Yan, Hongqiang 收藏  |  浏览/下载：16/0  |  提交时间：2020/11/15 Data acquisition  Forecasting  Hidden Markov models  Interpolation  Radial basis function networks  Stochastic systems  Systematic errors  Wind power  Auto-regressive integrated moving average  Cubic-spline interpolation  Data acquisition system  Empirical Mode Decomposition  Empirical mode decomposition method  Forecasting accuracy  Identification method  Wind speed sequences   Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE) 会议论文 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011, October 5, 2011 - October 7, 2011, Washington, DC, United states Dai Y.; Hu J.; Zhao D.; Zhu F. 收藏  |  浏览/下载：26/0  |  提交时间：2013/03/25 Traffic congestion leads to problems like delays  decreasing flow rate  and higher fuel consumption. Consequently  keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus  computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper  a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions. 2011 IEEE.   Research on fault tolerance based on neural networks 会议论文 2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3, 7th International Conference on Signal Processing, Beijing, PEOPLES R CHINA, Web of Science Kang, RX; Song, J; Zhang, YY 收藏  |  浏览/下载：4/0