Jian Wang
Jian Wang
Cross-Media Big Data Joint Lab, China University of Petroleum (East China)
Verified email at - Homepage
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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
Affine transformation-enhanced multifactorial optimization for heterogeneous problems
X Xue, K Zhang, KC Tan, L Feng, J Wang, G Chen, X Zhao, L Zhang, ...
IEEE Transactions on Cybernetics, 2020
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
History matching of naturally fractured reservoirs using a deep sparse autoencoder
K Zhang, J Zhang, X Ma, C Yao, L Zhang, Y Yang, J Wang, J Yao, H Zhao
SPE Journal 26 (04), 1700-1721, 2021
Data-driven niching differential evolution with adaptive parameters control for history matching and uncertainty quantification
X Ma, K Zhang, L Zhang, C Yao, J Yao, H Wang, W Jian, Y Yan
SPE Journal 26 (02), 993-1010, 2021
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
Multifidelity genetic transfer: an efficient framework for production optimization
F Yin, X Xue, C Zhang, K Zhang, J Han, BX Liu, J Wang, J Yao
SPE Journal 26 (04), 1614-1635, 2021
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 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
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
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
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
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
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
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
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
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