| Review and performance comparison of SVM-and ELM-based classifiers J Chorowski, J Wang, JM Zurada Neurocomputing 128, 507-516, 2014 | 164 | 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 | 157 | 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 | 100 | 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 | 94 | 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 | 90 | 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 | 87 | 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 | 86 | 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 | 66 | 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 | 59 | 2017 |
| 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 | 56 | 2021 |
| 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 | 56 | 2014 |
| 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 | 55 | 2020 |
| Deterministic convergence of conjugate gradient method for feedforward neural networks J Wang, W Wu, JM Zurada Neurocomputing 74 (14-15), 2368-2376, 2011 | 45 | 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 | 42 | 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 | 32 | 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 | 27 | 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 | 26 | 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 | 25 | 2012 |
| 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 | 24 | 2017 |
| 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 | 20 | 2017 |
