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浏览/检索结果: 共15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 Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm 期刊论文  OAI收割 MATHEMATICS, 2022, 卷号: 10, 期号: 10, 页码: - 作者:  Zhang, Jianguo;  Li, Peitao;  Yin, Xin;  Wang, Sheng;  Zhu, Yuanguang   |  收藏  |  浏览/下载：11/0  |  提交时间：2023/08/02 mind evolutionary algorithm  BP neural network  MEA-BP model  rock mechanical parameters  orthogonal test method   An improved back propagation neural network prediction model for subsurface drip irrigation system 期刊论文  OAI收割 COMPUTERS & ELECTRICAL ENGINEERING, 2017, 页码: 58-65 作者:  Gu, Jian;  Yin, Guanghua;  Huang, Pengfei;  Guo, Jinlu;  Chen, Lijun   |  收藏  |  浏览/下载：37/0  |  提交时间：2018/09/10 BP neural network  Genetic algorithm  Crop yield-irrigation model  Subsurface drip irrigation system   A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin 期刊论文  OAI收割 SPRINGERPLUS, 2016, 卷号: 5, 期号: 1, 页码: 1911 作者:  Liu, Longlong;  Zhao, Tingting;  Ma, Meng;  Wang, Yan;  Wang Y(王燕) 收藏  |  浏览/下载：28/0  |  提交时间：2017/01/13 BP algorithm  Gene regulatory network  Neural network model  Differentially expressed Sea Urchin genes   BP-AR-BASED HUMAN JOINT ANGLE ESTIMATION USING MULTI-CHANNEL SEMG 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION, 2015, 卷号: 30, 期号: 3, 页码: 227-237 作者:  Tong, Lina;  Zhang, Feng;  Hou, Zeng-Guang;  Wang, Weiqun;  Peng, Liang 收藏  |  浏览/下载：42/0  |  提交时间：2015/10/13 sEMG-joint angle estimation  rehabilitation  moving Butterworth filtering method  BP neural network  autoregressive (AR) model   Deep-seated Landslide Deformation Forecasting Model Based on the Time Series of Groundwater level 会议论文  OAI收割 the 2014 International Debris-Flow Workshop, National Chengkun University,Taiwan,Tainan, Otc.2-3,2014 作者:  Cui Y(崔云);  Kong JM(孔纪名) 收藏  |  浏览/下载：33/0  |  提交时间：2014/12/17 response characteristics  groundwater level observation  deep-seated landslide forecasting  BP neural network model   Environmental Pollution and its Influencing Factors in Mountainous and Hilly Rural Area of Sichuan Province in China 会议论文  OAI收割 2012 International Conference on Energy and Environmental Protection, 内蒙古呼和浩特, 2012-6-23~24 作者:  Shaoquan Liu;  Fangting Xie 收藏  |  浏览/下载：61/0  |  提交时间：2012/12/30 BP Neural Network Model  Influencing Factors  Mountainous and Hilly Rural Area  Rural Environmental Pollution  Sichuan   On grass yield remote sensing estimation models of China's northern farming-pastoral ecotone 会议论文  OAI收割 Advances in Intelligent and Soft Computing, 2012 作者:  Zhu Xiaohua 收藏  |  浏览/下载：21/0  |  提交时间：2012/12/01 Estimation  Models  Monitoring  nasa  Neural networks  Nonlinear systems  Remote sensing  Vegetation  BP neural network model  BP neural networks  China's northern farming-pastoral ecotone  Estimation models  Grass yield  Non-linear model  Sample data  Vegetation index  Yield conditions  Yield estimation   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.   Responses of River Runoff to Climate Change Based on Nonlinear Mixed Regression Model in Chaohe River Basin of Hebei Province, China 期刊论文  OAI收割 CHINESE GEOGRAPHICAL SCIENCE, 2010, 卷号: 20, 期号: 2, 页码: 152-158 作者:  Jiang Yan;  Liu Changming;  Zheng Hongxing;  Li Xuyong;  Wu Xianing 收藏  |  浏览/下载：22/0  |  提交时间：2015/07/30 river runoff  runoff forecast  nonlinear mixed regression model  linear multi-regression model  linear mixed regression model  BP neural network   Environment modeling of AS-R robot based on BP Neural Network (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Xie M.-J.; Yu X.-L.; Wang Z.-Q. 收藏  |  浏览/下载：34/0  |  提交时间：2013/03/25 There are some limitations when environment model of AS-R mobile robot is obtained by only using a separate CCD camera or ultrasonic sensor array. The BP neural network is designed to fuse information of the two sensors to obtain the robot's environment model in this paper. The input of BP neural network is the intercept and slope of the edge line obtained CCD camera and ultrasonic sensor arrays  which is processed by through the global coordinates of coordinate transformation. The output of BP network is the fused intercept and slope of the straight edge. Experiment shows that environment model is feasible for the AS-R mobile robot and the environment modeling method has more reliability and accuracy. 2010 IEEE.