机构

• 地理科学与资源研究所 [3]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2017 [3]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4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 Evaluation of intensive urban land use based on an artificial neural network model: A case study of Nanjing City, China 期刊论文  OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2017, 卷号: 27, 期号: 5, 页码: 735-746 作者:  Qiao Weifeng;  Gao Junbo;  Liu Yansui;  Qin Yueheng;  Lu Cheng   |  收藏  |  浏览/下载：22/0  |  提交时间：2019/09/25 urban land  intensive use  functional area  artificial neural network (ANN) model  Nanjing City   Evaluation of intensive urban land use based on an artificial neural network model: A case study of Nanjing City, China 期刊论文  OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2017, 卷号: 27, 期号: 5, 页码: 735-746 作者:  Qiao Weifeng;  Gao Junbo;  Liu Yansui;  Qin Yueheng;  Lu Cheng   |  收藏  |  浏览/下载：25/0  |  提交时间：2019/09/25 urban land  intensive use  functional area  artificial neural network (ANN) model  Nanjing City   Evaluation of intensive urban land use based on an artificial neural network model: A case study of Nanjing City, China 期刊论文  OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2017, 卷号: 27, 期号: 5, 页码: 735-746 作者:  Qiao Weifeng;  Gao Junbo;  Liu Yansui;  Qin Yueheng;  Lu Cheng   |  收藏  |  浏览/下载：26/0  |  提交时间：2019/09/25 urban land  intensive use  functional area  artificial neural network (ANN) model  Nanjing City   Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文  OAI收割 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China 作者:  Fan Y. 收藏  |  浏览/下载：37/0  |  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.