中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共5条，第1-5条

帮助

条数/页：排序方式：
	Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems 期刊论文 OAI收割 IEEE ACCESS, 2022, 卷号: 10, 页码: 49873-49891 作者: Huang, Yunhu; Chen, Dewang; Zhao, Wendi; Lv, Yisheng \| 收藏 \| 浏览/下载：34/0 \| 提交时间：2022/07/25 Computational modeling Training Microwave integrated circuits Deep learning Data models Artificial neural networks Training data Fuzzy c-means (FCM) clustering maximal information coefficient (MIC) random input (RI) deep patch learning classifier interpretability
	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：55/0 \| 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
	Input-based structure-specific proficiency predicts the neural mechanism of adult L2 syntactic processing 期刊论文 OAI收割 BRAIN RESEARCH, 2015, 卷号: 1610, 期号: 0, 页码: 42-50 作者: Deng, Taiping; Zhou, Huixia; Bi, Hong-Yan; Chen, Baoguo 收藏 \| 浏览/下载：35/0 \| 提交时间：2015/09/08 Input training Structure-specific proficiency Second language
	Research on the identification for a nonlinear system (EI CONFERENCE) 会议论文 OAI收割 International Conference on Optical, Electronic Materials and Applications 2011, OEMA 2011, March 4, 2011 - March 6, 2011, Chongqing, China 作者: Liu J.; Jia P.; Liu J.; Liu J. 收藏 \| 浏览/下载：37/0 \| 提交时间：2013/03/25 The characteristic of the drift error of inertial platform is a high-order nonlinear dynamic system using the neural networks' abilities of universal approximation of differentiable trajectory and capturing system dynamic information this paper presents the drift error identifying project of inertial platform based on Elman networks structure. First the drift error model of inertial platform is established after selecting the input and output for network momentum and alterable speed algorithm is used to speed up the network convergence. On the basis of the algorithm the extended nonlinear node function in the hidden network does not only improve the learning speed of network but also satisfies the need of accuracy on system identification. Through the drift error data measured on inertial platform the training result shows that the scheme achieves satisfied identification results. (2011) Trans Tech Publications.
	Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm (EI CONFERENCE) 会议论文 OAI收割 ICO20: Illumination, Radiation, and Color Technologies, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Z.; Zhou F.; Wang C.; Li Z. 收藏 \| 浏览/下载：39/0 \| 提交时间：2013/03/25 Munsell color system is selected to establish the mutual conversion between RGB and Lab* color model for camera. The color luminance meter and CCD camera synchronously measure the same color card XYZ value is gotten from the color luminance meter the training error is 0.000748566 it can show that the method combining second general revolving combination design with genetic algorithm can optimize the hidden-layer structure of neural network. Using the data of testing set to test this network and calculating the color difference between forecast value and true value the color picture captured from CCD camera is expressed for RGB value as the input of neural network and the Lab* value converted from XYZ value is regarded as the real color value of target card which the difference is not obvious comparing with forecast result the maximum is 5.6357 NBS namely the output of neural network. The neural network of two hidden-layers is considered the minimum is 0.5311 NBS so the second general revolving combination design is introduced into optimizing the structure of neural network and the average of color difference is 3.1744 NBS. which can carry optimization through unifying project design data processing and the precision of regression equation. Their mathematics model of encoding space is gained and the significance inspection shows the confidence degree of regression equation is 99%. The mathematics model is optimized by genetic algorithm optimization solution is gotten and function value of the goal is 0.0007168. The neural network of the optimization solution is trained