机构

• 地理科学与资源研究所 [3]
• 长春光学精密机械与物... [2]
• 力学研究所 [1]
• 计算技术研究所 [1]
• 遥感与数字地球研究所 [1]
• 沈阳自动化研究所 [1]
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采集方式

• OAI收割 [10]

内容类型

• 会议论文 [5]
• 期刊论文 [5]

发表日期

• 2023 [1]
• 2021 [1]
• 2019 [2]
• 2015 [1]
• 2012 [2]
• 2011 [2]
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学科主题

筛选

浏览/检索结果: 共10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 Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization 期刊论文  OAI收割 CHINA COMMUNICATIONS, 2023, 页码: 18 作者:  Chen, Haihua;  Zhang, Jingyao;  Jiang, Bin;  Cui, Xuerong;  Zhou, Rongrong   |  收藏  |  浏览/下载：18/0  |  提交时间：2023/12/04 Direction-of-arrival estimation  Estimation  Neural networks  Mathematical models  Training  Covariance matrices  Biological neural networks  particle swarm optimization (PSO) algorithm  PSO-BP neural network  gaussian colored noise  multiple sources  higher-order cumulant   Analysis and prediction of high-speed train wheel wear based on SIMPACK and backpropagation neural networks 期刊论文  OAI收割 EXPERT SYSTEMS, 2021, 卷号: 38, 期号: 7, 页码: 11 作者:  Wang, Shuwen;  Yan, Hao;  Liu, Caixia;  Fan, Ning;  Liu XM(刘小明)   |  收藏  |  浏览/下载：48/0  |  提交时间：2021/11/01 BP neural networks  high-speed train  SIMPACK  wheel wear   Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics 期刊论文  OAI收割 GEODERMA, 2019, 卷号: 354, 页码: 9 作者:  Tao, Huan;  Liao, Xiaoyong;  Zhao, Dan;  Gong, Xuegang;  Cassidy, Daniel P.   |  收藏  |  浏览/下载：75/0  |  提交时间：2020/03/23 Co-contaminated sites  Arsenic  PAHs  Backpropagation (BP) neural networks  Geostatistics  Nemerow pollution index   Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics 期刊论文  OAI收割 GEODERMA, 2019, 卷号: 354, 页码: 9 作者:  Tao, Huan;  Liao, Xiaoyong;  Zhao, Dan;  Gong, Xuegang;  Cassidy, Daniel P.   |  收藏  |  浏览/下载：15/0  |  提交时间：2020/03/23 Co-contaminated sites  Arsenic  PAHs  Backpropagation (BP) neural networks  Geostatistics  Nemerow pollution index   Day-ahead hourly photovoltaic generation forecasting using extreme learning machine 会议论文  OAI收割 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, June 8-12, 2015 作者:  Li ZW(李忠文);  Zang CZ(臧传治);  Zeng P(曾鹏);  Yu HB(于海斌);  Li HP(李鹤鹏) 收藏  |  浏览/下载：30/0  |  提交时间：2015/12/19 BP Neural Networks  Day-ahead  Photovoltaic  Forecasting  Extreme Learning Machine   An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文  OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者:  Yu P.;  Yu P. 收藏  |  浏览/下载：31/0  |  提交时间：2013/03/25 In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA)  genetic algorithm (GA)  and artificial neural networks (ANNs). First  some characteristics of the hyperspectral remotely sensed data  such as high correlation  high redundancy  etc.  are investigated. Based on the above analysis  we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next  we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem  we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.   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：35/0  |  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.   CORRELATION BETWEEN PM CONCENTRATIONS AND AEROSOL OPTICAL DEPTH IN EASTERN CHINA BASED ON BP NEURAL NETWORKS 会议论文  OAI收割 2011 Ieee International Geoscience and Remote Sensing Symposium, New York Wu, Yerong; Guo, Jianping; Zhang, Xin; Li, Xiaowen 收藏  |  浏览/下载：24/0  |  提交时间：2014/12/07 PM  MODIS  BP Neural Networks  PBLH  PARTICULATE MATTER  URBAN AIR  THICKNESS  PM2.5   S02 Emission Characteristics and BP Neural Networ Prediction in MSW/Coal Co-Fired Fluidized Beds 期刊论文  OAI收割 JOURNAL OF THERMAL SCIENCE, 2006, 卷号: 15, 期号: 3, 页码: 281,288 Junming WENl1 2; Jianhua YAN1; Dongping ZHANG1 3; Yong CHI1; Mingjiang NI1; Kefa CEN1 收藏  |  浏览/下载：17/0  |  提交时间：2013/01/31 municipal solid waste (MSW)  SO2 emission  fluidized bed  BP neural networks  prediction model.