| Randomized algorithms for nonlinear system identification with deep learning modification E De la Rosa, W Yu Information Sciences 364, 197-212, 2016 | 82 | 2016 |
| Data-driven fuzzy modeling using restricted Boltzmann machines and probability theory E De la Rosa, W Yu IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (7), 2316-2326, 2018 | 40 | 2018 |
| Nonlinear system modeling with deep neural networks and autoencoders algorithm E De la Rosa, W Yu, X Li 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016 | 20 | 2016 |
| Restricted Boltzmann machine for nonlinear system modeling E De la Rosa, W Yu 2015 IEEE 14th International conference on machine learning and applications …, 2015 | 14 | 2015 |
| Nonlinear system identification using deep learning and randomized algorithms E De la Rosa, W Yu, X Li 2015 IEEE International Conference on Information and Automation, 274-279, 2015 | 14 | 2015 |
| Neural Modeling With Guaranteed Input–Output Probability Distributions W Yu, E De la Rosa IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (11), 6660-6668, 2020 | 7 | 2020 |
| Deep Boltzmann machine for nonlinear system modelling W Yu, E De la Rosa International Journal of Machine Learning and Cybernetics 10, 1705-1716, 2019 | 7 | 2019 |
| Probability based fuzzy modeling B de la Rosa, W Yu, X Li 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2017 | 4 | 2017 |
| Fuzzy modeling from black-box data with deep learning techniques E De la Rosa, W Yu, H Sossa Advances in Neural Networks-ISNN 2017: 14th International Symposium, ISNN …, 2017 | 1 | 2017 |
| Conditional probability calculation using restricted Boltzmann machine with application to system identification E De la Rosa, W Yu arXiv preprint arXiv:1806.02499, 2018 | | 2018 |