Título: Data Driven Fuzzy Systems and Deep Neural Networks for Nonlinear Dynamic Processes Modeling
Autores: Daniel Leite, Igor Skrjanc & Fernando Gomide
Resumo: Data-driven modeling has become a predominant approach for learning and understanding systems. Models span a wide range of domains, including medical, health informatics, biological, environmental, meteorological, transportation, and economic systems, as well as complex dynamic virtual information, engineering, and hybrid systems. Computational intelligence techniques, including fuzzy systems, neural networks, evolutionary computation, and their hybridizations, are key driving forces in the current data-driven system modeling effort. This paper addresses data-driven fuzzy and neural modeling analytics and analyzes their performance in nonlinear dynamic systems modeling and prediction tasks. Data-driven fuzzy modeling and neural-based analytics are powerful paradigms that compete closely, often outperforming many alternative state-of-the-art methods, such as gradient boosting, kernel ridge regression, and Gaussian processes. In particular, the flexibility and approximation capabilities of data-driven fuzzy models, long-short-term memory, and convolutional neural networks are analyzed in two applications: (i) learning from a data stream produced by a synthetic nonlinear difference equation, and (ii) electricity load time series forecasting. The usefulness of the models is discussed in terms of their prediction accuracy and parametric complexity. In computational experiments, the recursive or incremental level-set fuzzy model provided both accuracy and interpretability with fewer parameters compared to the other methods, making it an attractive option for real-time modeling and forecasting tasks.
Palavras-chave: fuzzy systems; machine learning; neural networks; dynamic systems.
Páginas: 7
Código DOI: 10.21528/CBIC2025-1124026
Artigo em PDF: CBIC_2025_paper1124026.pdf
Arquivo BibTeX:
CBIC_2025_1124026.bib