中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共5条，第1-5条 帮助

限定条件                    
已选(0)清除 条数/页：   排序方式：
Improving Learning Efficiency of Recurrent Neural Network through Adjusting Weights of All Layers in a Biologically-inspired Framework 会议论文
, Anchorage, AK, USA, 2017-5-14
作者:  Huang, Xiao;  Wu, Wei;  Yin, Peijie;  Qiao, Hong
浏览  |  Adobe PDF(466Kb)  |  收藏  |  浏览/下载：228/55  |  提交时间：2020/06/09
Brain-inspired model  emotion  motion learning  recurrent neural network  
Improving learning efficiency of recurrent neural network through adjusting weights of all layers in a biologically-inspired framework 会议论文
, Anchorage, Alaska, 14-19 May 2017
作者:  Xiao Huang;  Wei Wu;  Peijie Yin;  Hong Qiao
浏览  |  Adobe PDF(463Kb)  |  收藏  |  浏览/下载：302/92  |  提交时间：2018/01/06
Emergency Event Web Information Acquisition using Crowd Web Sensors 期刊论文
WIRELESS PERSONAL COMMUNICATIONS, 2017, 卷号: 95, 期号: 3, 页码: 2393-2411
作者:  Wei, Xiao;  Hu, Hong;  Zeng, Daniel Dajun;  Wu, Wei
浏览  |  Adobe PDF(1864Kb)  |  收藏  |  浏览/下载：311/84  |  提交时间：2018/01/04
Emergency Event Management  Web Crawler  Crowd Sensor  Social Computing  Semantic Link Network  
SAR target configuration recognition based on the biologically inspired model 期刊论文
NEUROCOMPUTING, 2017, 期号: 234, 页码: 185-191
作者:  Huang, Xiayuan;  Nie, Xiangli;  Wu, Wei;  Qiao, Hong;  Zhang, Bo
浏览  |  Adobe PDF(1628Kb)  |  收藏  |  浏览/下载：370/110  |  提交时间：2017/05/05
Biologically Inspired Model  Sar Target Configuration Recognition  Episodic Features  Semantic Features  Aspect Angle Estimation  
Towards human-like and transhuman perception in AI 2.0: a review 期刊论文
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2017, 卷号: 18, 期号: 1, 页码: 58-67
作者:  Tian, Yong-hong;  Chen, Xi-lin;  Xiong, Hong-kai;  Li, Hong-liang;  Dai, Li-rong;  Chen, Jing;  Xing, Jun-liang;  Chen, Jing;  Wu, Xi-hong;  Hu, Wei-min;  Hu, Yu;  Huang, Tie-jun;  Gao, Wen
浏览  |  Adobe PDF(452Kb)  |  收藏  |  浏览/下载：381/67  |  提交时间：2017/05/05
Intelligent Perception  Active Vision  Auditory Perception  Speech Perception  Autonomous Learning