Jian Wang
Zitiert von
Zitiert von
Review and performance comparison of SVM-and ELM-based classifiers
J Chorowski, J Wang, JM Zurada
Neurocomputing 128, 507-516, 2014
Convergence analysis of online gradient method for BP neural networks
W Wu, J Wang, M Cheng, Z Li
Neural Networks 24 (1), 91-98, 2011
Fractional-order gradient descent learning of BP neural networks with Caputo derivative
J Wang, Y Wen, Y Gou, Z Ye, H Chen
Neural Networks 89, 19-30, 2017
Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs
X Shi, J Wang, G Liu, L Yang, X Ge, S Jiang
Journal of Natural Gas Science and Engineering 33, 687-702, 2016
Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks
W Wu, Q Fan, JM Zurada, J Wang, D Yang, Y Liu
Neural Networks 50, 72-78, 2014
An efficient approach for real-time prediction of rate of penetration in offshore drilling
X Shi, G Liu, X Gong, J Zhang, J Wang, H Zhang
Mathematical Problems in Engineering 2016, 2016
A novel pruning algorithm for smoothing feedforward neural networks based on group lasso method
J Wang, C Xu, X Yang, JM Zurada
IEEE transactions on neural networks and learning systems 29 (5), 2012-2024, 2017
Deterministic convergence of conjugate gradient method for feedforward neural networks
J Wang, W Wu, JM Zurada
Neurocomputing 74 (14-15), 2368-2376, 2011
Convergence of cyclic and almost-cyclic learning with momentum for feedforward neural networks
J Wang, J Yang, W Wu
IEEE Transactions on Neural Networks 22 (8), 1297-1306, 2011
Feature selection for neural networks using group lasso regularization
H Zhang, J Wang, Z Sun, JM Zurada, NR Pal
IEEE Transactions on Knowledge and Data Engineering 32 (4), 659-673, 2020
A novel conjugate gradient method with generalized Armijo search for efficient training of feedforward neural networks
J Wang, B Zhang, Z Sun, W Hao, Q Sun
Neurocomputing 275, 308-316, 2018
Computational properties and convergence analysis of BPNN for cyclic and almost cyclic learning with penalty
J Wang, W Wu, JM Zurada
Neural Networks 33, 127-135, 2012
Convergence analyses on sparse feedforward neural networks via group lasso regularization
J Wang, Q Cai, Q Chang, JM Zurada
Information Sciences 381, 250-269, 2017
Convergence analysis of BP neural networks via sparse response regularization
J Wang, Y Wen, Z Ye, L Jian, H Chen
Applied Soft Computing 61, 354-363, 2017
Convergence of gradient method for double parallel feedforward neural network
J Wang, W Wu, Z Li, L Li
Int J Numer Anal Model 8, 484-495, 2011
A new method for rock brittleness evaluation in tight oil formation from conventional logs and petrophysical data
X Shi, J Wang, X Ge, Z Han, G Qu, S Jiang
Journal of Petroleum Science and Engineering 151, 169-182, 2017
Brittleness index prediction in shale gas reservoirs based on efficient network models
X Shi, G Liu, Y Cheng, L Yang, H Jiang, L Chen, S Jiang, J Wang
Journal of Natural Gas Science and Engineering 35, 673-685, 2016
A modified gradient learning algorithm with smoothing L1/2 regularization for Takagi–Sugeno fuzzy models
Y Liu, W Wu, Q Fan, D Yang, J Wang
Neurocomputing 138, 229-237, 2014
A pruning algorithm with L 1/2 regularizer for extreme learning machine
Y Fan, W Wu, W Yang, Q Fan, J Wang
Journal of Zhejiang University SCIENCE C 15 (2), 119-125, 2014
Convergence analysis of Caputo-type fractional order complex-valued neural networks
J Wang, G Yang, B Zhang, Z Sun, Y Liu, J Wang
IEEE Access 5, 14560-14571, 2017
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