受强制性开放获取政策约束的文章 - Che Hangjun了解详情
无法在其他位置公开访问的文章：31 篇
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A collaborative neurodynamic approach to global and combinatorial optimization
H Che, J Wang
Neural Networks 114, 15-27, 2019
强制性开放获取政策： 中国科学院, 国家自然科学基金委员会, Research Grants Council, Hong Kong
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A two-timescale duplex neurodynamic approach to mixed-integer optimization
H Che, J Wang
IEEE Transactions on Neural Networks and Learning Systems 32 (1), 36-48, 2020
强制性开放获取政策： 国家自然科学基金委员会, Research Grants Council, Hong Kong
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A two-timescale duplex neurodynamic approach to biconvex optimization
H Che, J Wang
IEEE Transactions on Neural Networks and Learning Systems 30 (8), 2503-2514, 2018
强制性开放获取政策： 国家自然科学基金委员会, Research Grants Council, Hong Kong
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A nonnegative matrix factorization algorithm based on a discrete-time projection neural network
H Che, J Wang
Neural Networks 103, 63-71, 2018
强制性开放获取政策： 国家自然科学基金委员会, Research Grants Council, Hong Kong
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Bicriteria sparse nonnegative matrix factorization via two-timescale duplex neurodynamic optimization
H Che, J Wang, A Cichocki
IEEE Transactions on Neural Networks and Learning Systems 34 (8), 4881-4891, 2021
强制性开放获取政策： 国家自然科学基金委员会, Research Grants Council, Hong Kong
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A recurrent neural network for optimal real-time price in smart grid
X He, T Huang, C Li, H Che, Z Dong
Neurocomputing 149, 608-612, 2015
强制性开放获取政策： 国家自然科学基金委员会
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Task assignment for multivehicle systems based on collaborative neurodynamic optimization
J Wang, J Wang, H Che
IEEE Transactions on Neural Networks and Learning Systems 31 (4), 1145-1154, 2019
强制性开放获取政策： 中国科学院, 国家自然科学基金委员会, Research Grants Council, Hong Kong
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Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning
B Pan, C Li, H Che
Neural Networks 161, 638-658, 2023
强制性开放获取政策： 国家自然科学基金委员会
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Tensor-based adaptive consensus graph learning for multi-view clustering
W Guo, H Che, MF Leung
IEEE Transactions on Consumer Electronics, 2024
强制性开放获取政策： 国家自然科学基金委员会
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Exponential convergence of a proximal projection neural network for mixed variational inequalities and applications
X Ju, H Che, C Li, X He, G Feng
Neurocomputing 454, 54-64, 2021
强制性开放获取政策： 国家自然科学基金委员会
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An intelligent method of swarm neural networks for equalities-constrained nonconvex optimization
H Che, C Li, X He, T Huang
Neurocomputing 167, 569-577, 2015
强制性开放获取政策： 国家自然科学基金委员会
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Adaptive graph nonnegative matrix factorization with the self-paced regularization
X Yang, H Che, MF Leung, C Liu
Applied Intelligence 53 (12), 15818-15835, 2023
强制性开放获取政策： 国家自然科学基金委员会
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A proximal neurodynamic network with fixed-time convergence for equilibrium problems and its applications
X Ju, C Li, H Che, X He, G Feng
IEEE Transactions on Neural Networks and Learning Systems 34 (10), 7500-7514, 2022
强制性开放获取政策： 国家自然科学基金委员会
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Linear impulsive control system with impulse time windows
Y Feng, J Yu, C Li, T Huang, H Che
Journal of Vibration and Control 23 (1), 111-118, 2017
强制性开放获取政策： 国家自然科学基金委员会
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Sparse signal reconstruction via collaborative neurodynamic optimization
H Che, J Wang, A Cichocki
Neural Networks 154, 255-269, 2022
强制性开放获取政策： 国家自然科学基金委员会, Research Grants Council, Hong Kong
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A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction
C Dai, H Che, MF Leung
International Journal on Artificial Intelligence Tools 30 (01), 2140007, 2021
强制性开放获取政策： 国家自然科学基金委员会
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A recurrent neural network for adaptive beamforming and array correction
H Che, C Li, X He, T Huang
Neural Networks 80, 110-117, 2016
强制性开放获取政策： 国家自然科学基金委员会
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Two-timescale neurodynamic approaches to supervised feature selection based on alternative problem formulations
Y Wang, J Wang, H Che
Neural Networks 142, 180-191, 2021
强制性开放获取政策： 国家自然科学基金委员会, Research Grants Council, Hong Kong
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Solving mixed variational inequalities via a proximal neurodynamic network with applications
X Ju, H Che, C Li, X He
Neural Processing Letters 54 (1), 207-226, 2022
强制性开放获取政策： 国家自然科学基金委员会
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A collaborative neurodynamic approach to sparse coding
H Che, J Wang, W Zhang
International Symposium on Neural Networks, 454-462, 2019
强制性开放获取政策： 中国科学院, 国家自然科学基金委员会, Research Grants Council, Hong Kong
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