中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共6条，第1-6条

帮助

条数/页：排序方式：
	Modelling Soil δ¹³C across the Tibetan Plateau Using Deep-Learning 期刊论文 OAI收割 JOURNAL OF ENVIRONMENTAL INFORMATICS, 2024, 卷号: 44, 期号: 1, 页码: 48-60 作者: Zhou, T.; Lai, Y. S.; Yang, Z. H.; Shi, Y. H.; Luo, X. R. \| 收藏 \| 浏览/下载：4/0 \| 提交时间：2024/10/08 soil delta C-13 spatial variability multi-layer perceptron neural network soil carbon turnover Tibetan Plateau biogeochemical cycles biogeochemical cycles
	Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network 期刊论文 OAI收割 REMOTE SENSING, 2021, 卷号: 13, 期号: 2, 页码: 18 作者: Wang, He; Yang, Jingsong; Zhu, Jianhua; Ren, Lin; Liu, Yahao \| 收藏 \| 浏览/下载：51/0 \| 提交时间：2021/04/21 Advanced Scatterometer (ASCAT) significant wave height WaveWatch III deep learning multi-hidden-layer neural network
	Adaptive Synchronization of Delayed Memristive Neural Networks With Unknown Parameters 期刊论文 OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 卷号: 50, 期号: 2, 页码: 539-549 作者: Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2020/03/30 Synchronization Biological neural networks Neurons Adaptive systems Multi-layer neural network Memristors Adaptive control delayed memristive neural networks global asymptotic synchronization unknown parameters
	Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm 期刊论文 OAI收割 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 1, 页码: 108-122 作者: Sajjad Afrakhteh; Mohammad-Reza Mosavi; Mohammad Khishe; Ahmad Ayatollahi \| 收藏 \| 浏览/下载：21/0 \| 提交时间：2021/02/22 Brain-computer interface (BCI) classification electroencephalography (EEG) gravitational search algorithm (GSA) multi-layer perceptron neural network (MLP-NN) particle swarm optimization.
	Reconstruction of RANS model and cross-validation of flow field based on tensor basis neural network 会议论文 OAI收割 San Francisco, CA, United states, July 28, 2019 - August 1, 2019 作者: Song XD; Zhang Z(张珍); Wang YW(王一伟); Ye SR(叶舒然); Huang CG(黄晨光) \| 收藏 \| 浏览/下载：89/0 \| 提交时间：2020/11/20 Cross-validation Multi-layer neural network Reynolds stress Turbulence model
	A MLP-PNN neural network for CCD image super-resolution in wavelet packet domain (EI CONFERENCE) 会议论文 OAI收割 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12, 2008 - October 14, 2008, Dalian, China Zhao X.; Fu D.; Zhai L. 收藏 \| 浏览/下载：68/0 \| 提交时间：2013/03/25 Image super-resolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First decompose and reconstruct the image by wavelet packet. Before constructing the image use neural network in place of other rebuilding method to reconstruct the coefficients in the wavelet packet domain. Second probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data in the wavelet packet domain. The network kernel function is optimally determined for this problem by a MLP-PNN (Multi Layer Perceptron - Probabilistic Neural Network) trained on synthetic data. Network parameters dependent on the sequence noise level. This super-sampled image is spatially Altered to correct finite pixel size effects to yield the final high-resolution estimate. This method can decrease the calculation cost and get perfect PSNR. Results are presented showing the quality of the proposed method. 2008 IEEE.