Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
The nature of statistical learning theory
The nature of statistical learning theory
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Online fuzzy logic crack detection of a cantilever beam
International Journal of Knowledge-based and Intelligent Engineering Systems
Density-based averaging - A new operator for data fusion
Information Sciences: an International Journal
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An approach to the on-line design of Takagi-Sugeno type fuzzy models is presented in the paper. It combines supervised and unsupervised learning and recursively updates both the model structure and parameters. The rule-base gradually evolves increasing its summarization power. This approach leads to the concept of the evolving Takagi Sugeno model. Due to the gradual update of the rule structure and parameters, it adapts to the changing data pattern. The requirement for update of the rule-base is based on the spatial proximity and is a quite strong one. As a result, the model evolves to a compact set of fuzzy rules, which adds to the interpretability, a property especially useful in fault detection. Other possible areas of application are adaptive non-linear control, time series forecasting, knowledge extraction, robotics, behavior modeling. The results of application to the on-line modeling the fermentation of Kluyveromyces lactis illustrate the efficiency of the approach.