Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
| Autor: | János Abonyi, Robert Babuska, F. Szeifert |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Fuzzy clustering
Fuzzy set General Medicine Fuzzy control system Mixture model computer.software_genre Fuzzy logic Computer Science Applications Human-Computer Interaction Control and Systems Engineering Expectation–maximization algorithm Data mining Electrical and Electronic Engineering Cluster analysis Multidimensional systems computer Algorithm Software Information Systems Mathematics |
| Zdroj: | IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). 32:612-621 |
| ISSN: | 1083-4419 |
| Popis: | The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature. |
| Databáze: | OpenAIRE |
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