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http://repositorio.ufla.br/jspui/handle/1/42414| Title: | Optimal rule-based granular systems from data streams |
| Keywords: | Granular coverage of the data Gaussian model Nonstationary environments Sistemas granulares Modelo gaussiano Ambientes não-estacionários |
| Issue Date: | Mar-2020 |
| Publisher: | IEEE – Institute of Electrical and Electronic Engineers |
| Citation: | LEITE, D. et al. Optimal Rule-Based Granular Systems From Data Streams. IEEE Transactions on Fuzzy Systems, Piscataway, v. 28, n. 3, p. 583-596, Mar. 2020. DOI: 10.1109/TFUZZ.2019.2911493. |
| Abstract: | We introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular coverage of the data. We use α-level sets over Gaussian membership functions to set model granularity and operate with hyperrectangular forms of granules in nonstationary environments. The resulting rule-based systems are formed in a formal and systematic fashion. They can be useful in time series modeling, dynamic system identification, predictive analytics, and adaptive control. Precise estimates and enclosures are given by linear piecewise and inclusion functions related to optimal granular mappings. |
| URI: | https://ieeexplore.ieee.org/document/8691724/authors#authors http://repositorio.ufla.br/jspui/handle/1/42414 |
| Appears in Collections: | DAT - Artigos publicados em periódicos DEG - Artigos publicados em periódicos |
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