@inproceedings{0bca59aea2ea44458e8939ba5a6816b9,
title = "Fuzzy modeling from black-box data with deep learning techniques",
abstract = "Deep learning techniques have been successfully used for pattern classification. These advantage methods are still not applied in fuzzy modeling. In this paper, a novel data-driven fuzzy modeling approach is proposed. The deep learning methods is applied to learn the probability properties of input and output pairs. We propose special unsupervised learning methods for these two deep learning models with input data. The fuzzy rules are extracted from these properties. These deep learning based fuzzy modeling algorithms are validated with three benchmark examples.",
keywords = "Black-box modeling, Deep learning, Fuzzy system",
author = "{de la Rosa}, Erick and Wen Yu and Humberto Sossa",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th International Symposium on Neural Networks, ISNN 2017 ; Conference date: 21-06-2017 Through 26-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59072-1_36",
language = "Ingl{\'e}s",
isbn = "9783319590714",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "304--312",
editor = "Andrew Leung and Fengyu Cong and Qinglai Wei",
booktitle = "Advances in Neural Networks - ISNN 2017 - 14th International Symposium, ISNN 2017, Proceedings",
address = "Alemania",
}