Optimal Rule-Based Granular Systems From Data Streams
| Autor: | Goran Andonovski, Fernando Gomide, Igor Škrjanc, Daniel Leite |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Adaptive control Computer science Data stream mining Applied Mathematics Granular computing System identification Rule-based system 02 engineering and technology Multi-objective optimization Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Piecewise 020201 artificial intelligence & image processing Granularity |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 28:583-596 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2019.2911493 |
| Popis: | 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 $\alpha$ -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. |
| Databáze: | OpenAIRE |
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