兰州大学机构库

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Review of pulse-coupled neural networks 期刊论文
IMAGE AND VISION COMPUTING, 2010, 卷号: 28, 期号: 1, 页码: 5-13
Authors:  Wang, ZB(王兆滨);  Ma, YD(马义德);  Cheng, FY;  Yang, LZ;  Ma, YD (reprint author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China.
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Pulse-coupled neural networks (PCNN)  Image processing  Artificial neural network  
Feed-back neural networks with discrete weights 期刊论文
NEURAL COMPUTING & APPLICATIONS, 2013, 卷号: 22, 期号: 6, 页码: 1063-1069
Authors:  Wang, LJ;  Zhou, QG(周庆国);  Jin, T;  Zhao, H;  Zhou, QG (reprint author), Lanzhou Univ, Sch Informat Sci & Engineer, Lanzhou 730000, Peoples R China.
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Monte Carlo Adaptation  Chaos phase  Pure memory phase  Mixture phase  Discrete weights  Feed-back neural network  
Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function 期刊论文
IEEE Access, 2020, 卷号: 8, 页码: 89281-89290
Authors:  Z. Huang;  X. Du;  L. Chen;  Y. Li;  M. Liu;  Y. Chou;  L. Jin
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Convolutional neural network  complex networks  randomly generated graph  network generator  brain tumors  
Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification with a Modified Activation Function 期刊论文
IEEE Access, 2020, 卷号: 8, 页码: 89281-89290
Authors:  Huang, Zhiguan;  Du, Xiaohao;  Chen, Liangming;  Li, Yuhe;  Liu, M(刘梅);  Chou, Yao;  Jin, L(金龙)
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Dynamics of high-order BAM neural networks with and without impulses 期刊论文
APPLIED MATHEMATICS AND COMPUTATION, 2009, 卷号: 215, 期号: 6, 页码: 2120-2133
Authors:  Huo, HF;  Li, WT(李万同);  Tang, SY;  Huo, HF (reprint author), Lanzhou Univ Technol, Inst Appl Math, Lanzhou 730050, Gansu, Peoples R China.
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Impulsive equations  High-order neural networks  Coincidence degree  Lyapunov functional  Global stability  
Pattern recognition using asymmetric attractor neural networks 期刊论文
Physical Review E: statistical, nonlinear, and soft matter physics, 2005, 卷号: 72, 期号: 6, 页码: -
Authors:  Jin, T;  Zhao, H;  Jin, T (reprint author), Lanzhou Univ, Dept Phys, Lanzhou 730000, Peoples R China.
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Effects of Dietary Energy Levels on Rumen Fermentation, Microbial Diversity, and Feed Efficiency of Yaks (Bos grunniens) 期刊论文
Frontiers in Microbiology, 2020, 卷号: 11
Authors:  Ahmad, Anum Ali;  Yang, Chao;  Zhang, Jianbo;  Kalwar, Qudratullah;  Liang, Zeyi;  Li, Chen;  Du, Mei;  Yan, Ping;  Long, Ruijun;  Han, Jianlin...Ahmad, Anum Ali;  Yang, Chao;  Zhang, Jianbo;  Kalwar, Qudratullah;  Liang, Zeyi;  Li, Chen;  Du, Mei;  Yan, Ping;  Long, Ruijun;  Han, Jianlin;  Ding, Xuezhi
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yak  dietary energy  rumen microbiota  volatile fatty acid  feed efficiency  growth performance  
Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China 期刊论文
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 卷号: 27, 期号: 23, 页码: 28931-28948
Authors:  Zhao, ZL(赵志立);  Qin, Jian;  He, Zhaoshuang;  Li, Huan;  Yang, Y(杨裔);  Zhang, RS(张瑞生)
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Air quality  Machine learning  Serial data  SSA  Sliding window  Neural network  
Quantitative structure-property relationship studies for estimating boiling points of alcohols using calculated molecular descriptors with radial basis function neural networks 期刊论文
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2004, 卷号: 72, 期号: 1
Authors:  Li, QF;  Chen, XG(陈兴国);  Hu, ZD(胡之德);  Li, QF (reprint author), Univ Marburg, Dept Chem, D-35032 Marburg, Germany.
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radial basis function (RBF) neural networks  boiling point  calculated molecular descriptors  alcohol compounds  
Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method 期刊论文
IEEE Access, 2020, 卷号: 8, 期号: *, 页码: 202459-202468
Authors:  Lou, Xiao;  Zhu, Youzhe;  Punithakumar, Kumaradevan;  Le, Lawrence H.;  Li, Baosheng
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Computed Tomography  Deep Learning  Esophagus  Segmentation  U-net  Automation  Computerized tomography  Deep learning  Image segmentation  Learning systems  Three dimensional computer graphicsComputed tomography images  Convex-hull approach  Experimental evaluation  Level set algorithm  Segmentation methods  Segmentation results  Semiautomatic methods  Topological levels