Abstract
This paper addresses data-driven fuzzy modeling using the concept of level sets. The focus is on nonlinear systems approximation and time series forecasting. Data-driven fuzzy modeling using level sets is a novel fuzzy modeling paradigm that differs from previous fuzzy modeling paradigms in the way the fuzzy rules construction and processing is done. The level set method outputs the weighted average of functions that map rule activation levels in a point in the output space. The data driven level set method is a simple and effective way to develop accurate and interpretable models. The method is used to model a nonlinear system, and to forecast a time series of actual electricity load data. Computational experiments show that the data-driven level set method outperforms state of the art machine learning algorithms.
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Acknowledgements
The last authors are grateful to the Brazilian National Council for Scientific and Technological Development for grants 304274/2019-4 and 302467/2019-0, respectively. The authors thank the reviewers for the comments that helped to improve the paper.
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Maciel, L., Ballini, R., Gomide, F. (2023). Data Driven Level Set Method in Fuzzy Modeling and Forecasting. In: Dick, S., Kreinovich, V., Lingras, P. (eds) Applications of Fuzzy Techniques. NAFIPS 2022. Lecture Notes in Networks and Systems, vol 500. Springer, Cham. https://doi.org/10.1007/978-3-031-16038-7_14
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DOI: https://doi.org/10.1007/978-3-031-16038-7_14
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