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Fuzzy Sets and Systems
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Fuzzy Sets and Systems - Information processing
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Neural Processing Letters
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Information Sciences: an International Journal
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HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
An Evolving Fuzzy Predictor for Industrial Applications
IEEE Transactions on Fuzzy Systems
Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section
IEEE Transactions on Fuzzy Systems
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In this paper, we propose a new online identification approach for evolving Takagi-Sugeno (TS) fuzzy models. Here, for a TS model, a certain number of models as neighboring models are defined and then the TS model switches to one of them at each stage of evolving. We define neighboring models for an in-progress (current) TS model as its fairly evolved versions, which are different with it just in two fuzzy rules. To generate neighboring models for the current model, we apply specially designed split and merge operations. By each split operation, a fuzzy rule is replaced with two rules; while by each merge operation, two fuzzy rules combine to one rule. Among neighboring models, the one with the minimum sum of squared errors - on certain time intervals - replaces the current model. To reduce the computational load of the proposed evolving TS model, straightforward relations between outputs of neighboring models and that of current model are established. Also, to reduce the number of rules, we define and use first-order TS fuzzy models whose generated local linear models can be localized in flexible fuzzy subspaces. To demonstrate the improved performance of the proposed identification approach, the efficiency of the evolving TS model is studied in prediction of monthly sunspot number and forecast of daily electrical power consumption. The prediction and modeling results are compared with that of some important existing evolving fuzzy systems.