Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
Granular approach for evolving system modeling
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A transformed input-domain approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section
IEEE Transactions on Fuzzy Systems
Multivariable Gaussian Evolving Fuzzy Modeling System
IEEE Transactions on Fuzzy Systems
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
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Thermal models for distribution transformers with core immersed in oil are of utmost importance for transformers lifetime study. The hot spot temperature determines the degradation speed of the insulating paper. High temperatures cause loss of mechanical stiffness, generating failures. Since the paper is the most fragile component of the transformer, its degradation determines the lifetime limits. Thus, good thermal models are needed to generate reliable data for lifetime forecasting methodologies. It is also desired that thermal models are able to adapt to cope with changes in the transformer behavior due to structural changes, maintenance and so on. In this work we apply an evolving fuzzy model to build adaptive thermal models of distribution transformers. The model used is able to adapt its parameters and also its structure based on a stream of data. The proposed model is evaluated using actual data from an experimental transformer. The results suggest that evolving fuzzy models are a promising approach for adaptive thermal modeling of distribution transformers.