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Erick De la Rosa
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Journal Articles
- 2021
- [j4]Wen Yu, Erick De la Rosa:
Neural Modeling With Guaranteed Input-Output Probability Distributions. IEEE Trans. Syst. Man Cybern. Syst. 51(11): 6660-6668 (2021) - 2020
- [j3]Erick De la Rosa, Wen Yu:
Data-Driven Fuzzy Modeling Using Restricted Boltzmann Machines and Probability Theory. IEEE Trans. Syst. Man Cybern. Syst. 50(7): 2316-2326 (2020) - 2019
- [j2]Wen Yu, Erick De la Rosa:
Deep Boltzmann machine for nonlinear system modelling. Int. J. Mach. Learn. Cybern. 10(7): 1705-1716 (2019) - 2016
- [j1]Erick De la Rosa, Wen Yu:
Randomized algorithms for nonlinear system identification with deep learning modification. Inf. Sci. 364-365: 197-212 (2016)
Conference and Workshop Papers
- 2017
- [c5]Erick De la Rosa, Wen Yu, Humberto Sossa:
Fuzzy Modeling from Black-Box Data with Deep Learning Techniques. ISNN (1) 2017: 304-312 - [c4]Erick De la Rosa, Wen Yu, Xiaoou Li:
Probability based fuzzy modeling. SMC 2017: 1633-1638 - 2016
- [c3]Erick De la Rosa, Wen Yu, Xiaoou Li:
Nonlinear system modeling with deep neural networks and autoencoders algorithm. SMC 2016: 2157-2162 - 2015
- [c2]Erick De la Rosa, Wen Yu, Xiaoou Li:
Nonlinear system identification using deep learning and randomized algorithms. ICIA 2015: 274-279 - [c1]Erick De la Rosa, Wen Yu:
Restricted Boltzmann Machine for Nonlinear System Modeling. ICMLA 2015: 443-446
Informal and Other Publications
- 2018
- [i2]Erick De la Rosa, Wen Yu:
Conditional probability calculation using restricted Boltzmann machine with application to system identification. CoRR abs/1806.02499 (2018) - 2017
- [i1]Erick De la Rosa, Wen Yu:
Data-Driven Fuzzy Modeling Using Deep Learning. CoRR abs/1702.07076 (2017)
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