An approach to online identification of Takagi-Sugeno fuzzy models

@article{Angelov2004AnAT,
  title={An approach to online identification of Takagi-Sugeno fuzzy models},
  author={P. Angelov and D. P. Filev},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  year={2004},
  volume={34},
  pages={484-498}
}
An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become… CONTINUE READING
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