Divulgação de evento

[Sistemas Fuzzy]

Prezada comunidade,

A pedido do comitê organizador, compartilhamos o evento abaixo. 

Vejam também o cartaz de divulgação anexo.

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17th APCA International Conference on Automatic Control and Soft Computing (CONTROLO 2026)

Organised by

Daniel Leite, University of Paderborn, Germany, daniel...@uni-paderborn.de
Igor Skrjanc, University of Ljubljana, Slovenia, igor.s...@fe.uni-lj.si
Jerome Mendes, University of Coimbra, Portugal, jerome...@uc.pt
Fernando Gomide, University of Campinas, Brazil, gom...@unicamp.br

Call for Papers
Description:
Many real-world systems operate under nonstationary conditions, where statistical properties evolve over time due to environmental and system changes, aging, operational variability, or disturbances. Traditional machine learning and control approaches, which rely on static models, offline multi-epoch training, or fixed-structure adaptive schemes, often fail to maintain performance when confronted with continuously evolving data. Incremental learning of model structures and parameters provides a framework to address these challenges by allowing predictors, classifiers, and intelligent feedback controllers to evolve sequentially with a higher
degree of autonomy and flexibility. These methods allow models to track drifts and emerging patterns, operate in real time without requiring retraining from scratch or access to historical data, and mitigate catastrophic forgetting while maintaining robustness and interpretability.
This session aims to bring together researchers working on incremental and continual machine learning methods for time series prediction, evolving system identification, and structurally adaptive models and controllers for nonstationary systems observed from data streams. The session welcomes theoretical advances, algorithmic developments, and practical applications of incremental learning in streaming environments, intelligent and evolving systems, and dynamical systems. Particular emphasis is given to methods capable of adapting model structures, parameters, and knowledge representations over time, including granular, fuzzy, neural, modular, and hybrid approaches. The goal is to foster interaction between the
machine learning and control communities and to promote new methodologies that allow reliable prediction, monitoring, and control in continuously evolving environments.

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Atenciosamente,

Vinícius Wasques e Graciele Silveira
Comitê Temático de Sistemas Fuzzy
Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)