机构

• 心理研究所 [3]
• 长春光学精密机械与物... [1]
• 自动化研究所 [1]

采集方式

• OAI收割 [5]

内容类型

• 会议论文 [4]
• 期刊论文 [1]

发表日期

• 2019 [1]
• 2017 [2]
• 2016 [1]
• 2011 [1]

学科主题

• 感知觉心理学 [1]
• 视觉心理物理与模型（... [1]
• 视觉注意、学习与记忆 [1]

筛选

浏览/检索结果: 共5条，第1-5条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择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作者升序作者降序 Adversarial image generation by combining content and style 期刊论文  OAI收割 IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 14, 页码: 2716-2723 作者:  Liu, Songyan;  Zhao, Chaoyang;  Gao, Yunze;  Wang, Jinqiao;  Tang, Ming   |  收藏  |  浏览/下载：54/0  |  提交时间：2020/03/30 image recognition  feature extraction  learning (artificial intelligence)  image texture  adversarial image generation  unique style  reference images  style feature extraction module  style specific image generation model  double-cycle training strategy  natural-content pairs  input natural images  style exchange  style-exchanged images  licence-plate image  handbags images   Teaching visual orientation discrimination through tactile learning 会议论文  OAI收割 曲阜, 2017.7.1 作者:  Ding-Zhi Hu;  Guo-Zhen Liu;  Cong Yu   |  收藏  |  浏览/下载：45/0  |  提交时间：2017/12/25 Perceptual Learning  concept learning  orientation  double training   Mutual transfer between visual and auditory temporal interval learning supports a central clock in temporal processing 会议论文  OAI收割 曲阜, 2017.7.2 作者:  Shu-Chen Guan;  Ying-Zi Xiong;  Cong Yu   |  收藏  |  浏览/下载：31/0  |  提交时间：2017/12/27 temporal interval discrimination  cross-modal  double training   双训练范式引起知觉学习迁移的心理物理学机制 会议论文  OAI收割 2016年第一届北京视觉科学会议, 北京, 2016-07 作者:  Yin-Yu Xie;  Rui Wang;  Cong Yu 收藏  |  浏览/下载：48/0  |  提交时间：2017/01/09 perceptual learning  double training  psychophysical mechanisms   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：42/0  |  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.