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Titlebook: Neural Information Processing; 22nd International C Sabri Arik,Tingwen Huang,Qingshan Liu Conference proceedings 2015 Springer Internationa

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发表于 2025-3-21 19:43:20 | 显示全部楼层 |阅读模式
书目名称Neural Information Processing
副标题22nd International C
编辑Sabri Arik,Tingwen Huang,Qingshan Liu
视频videohttp://file.papertrans.cn/664/663583/663583.mp4
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Neural Information Processing; 22nd International C Sabri Arik,Tingwen Huang,Qingshan Liu Conference proceedings 2015 Springer Internationa
描述.The four volume set LNCS 9489, LNCS 9490, LNCS 9491, andLNCS 9492 constitutes the proceedings of the 22nd International Conference onNeural Information Processing, ICONIP 2015, held in Istanbul, Turkey, inNovember 2015...The 231 full papers presented were carefully reviewed andselected from 375 submissions. The 4 volumes represent topical sectionscontaining articles on Learning Algorithms and Classification Systems;Artificial Intelligence and Neural Networks: Theory, Design, and Applications;Image and Signal Processing; and Intelligent Social Networks..
出版日期Conference proceedings 2015
关键词biometrics; data mining; genetic algorithm; pattern recognition; semantic Web; artificial neural networks
版次1
doihttps://doi.org/10.1007/978-3-319-26555-1
isbn_softcover978-3-319-26554-4
isbn_ebook978-3-319-26555-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Neural Information Processing978-3-319-26555-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/663583.jpg
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https://doi.org/10.1007/978-3-319-26555-1biometrics; data mining; genetic algorithm; pattern recognition; semantic Web; artificial neural networks
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Robust Tracking Control of Uncertain Nonlinear Systems Using Adaptive Dynamic Programming,tems. By selecting a discounted value function for the nominal augmented error system, we transform the robust tracking control problem into an optimal control problem. The control matrix is not required to be invertible by using the present method. Meanwhile, we employ a single critic neural networ
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Moving Target Tracking Based on Pulse Coupled Neural Network and Optical Flow,rring between moving targets and background. By using fusion ability of pulse coupled neural network (PCNN), the target regions and the background regions are fused respectively. Firstly using PCNN fuses the direction of the optical flow fusing, and extracts moving targets from video especially with
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