Titlebook: Advances in Neural Networks – ISNN 2020; 17th International S Min Han,Sitian Qin,Nian Zhang Conference proceedings 2020 Springer Nature Swi

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書目名稱Advances in Neural Networks – ISNN 2020影響因子(影響力)




書目名稱Advances in Neural Networks – ISNN 2020影響因子(影響力)學(xué)科排名




書目名稱Advances in Neural Networks – ISNN 2020網(wǎng)絡(luò)公開度




書目名稱Advances in Neural Networks – ISNN 2020網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Neural Networks – ISNN 2020被引頻次




書目名稱Advances in Neural Networks – ISNN 2020被引頻次學(xué)科排名




書目名稱Advances in Neural Networks – ISNN 2020年度引用




書目名稱Advances in Neural Networks – ISNN 2020年度引用學(xué)科排名




書目名稱Advances in Neural Networks – ISNN 2020讀者反饋




書目名稱Advances in Neural Networks – ISNN 2020讀者反饋學(xué)科排名

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Mercy Nkatha,Jonas Yawovi Dzinekkouaic module, is composed of solar cells in series connection and parallel connection. Photovoltaic module directly converts the light energy of the sun into electric energy. The construction of solar cell model needs precise parameters to support. This paper proposes using classification particle swa

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模范

https://doi.org/10.1007/978-981-15-0383-2ple-Size (HDLSS) problem in modelling. In this work, we extend the state-of-the-art multi-resolution SSM approach from two dimension (2D) to three dimension (3D) and from single organ to multiple organs. Then we proposed a multi-resolution multi-organ 3D SSM method that uses a downsampling-and-inter

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https://doi.org/10.1007/978-981-15-0383-2m of multiple local convex functions, which can be nonsmooth. Based on graph theory and nonsmooth analysis, we propose a neural network with a time-varying auxiliary function. The boundedness of the state solution is demonstrated by using the properties of the auxiliary function. Moreover, it is pro

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https://doi.org/10.1007/978-981-15-0383-2 directly predict the pose from the point cloud in a data-driven way remains an open question. In this paper, we present a deep learning-based laser odometry system that consists of a network pose estimation and a local map pose optimization. The network consumes the original 3D point clouds directl