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浏览/检索结果: 共3条，第1-3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 Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview 期刊论文 IEEE TRANSACTIONS ON CYBERNETICS, 2019, 卷号: 49, 页码: 2720-2731 作者:  Han, Min;  Zhong, Kai;  Qiu, Tie;  Han, Bing 收藏  |  浏览/下载：13/0  |  提交时间：2019/12/02 Big data  chaotic time series prediction  computational intelligence (CI)  interval type-2 fuzzy neural network (IT2FNN)   Computational Intelligence in Urban Traffic Signal Control: A Survey 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2012, 卷号: 42, 期号: 4, 页码: 485-494 作者:  Zhao, Dongbin;  Dai, Yujie;  Zhang, Zhen 收藏  |  浏览/下载：30/0  |  提交时间：2015/08/12 Computational intelligence (CI)  freeway network  surface-way network  traffic congestions  traffic signal control (TSC)   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：24/0  |  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.