Abstract : We consider a Takagi-Sugeno-Kang (TSK) fuzzy rule based system used to model a memory-less nonlinearity from numerical data. We develop a simple and effective technique allowing to remove irrelevant inputs, choose a number of membership functions for each input, propose well estimated starting values of membership functions and consequent parameters. All this will make the fuzzy model more concise and transparent. The final training procedure will be shorter and more effective.
Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.62-69, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_11〉
https://hal.inria.fr/hal-01060651
Contributeur : Hal Ifip
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Dernière modification le : dimanche 17 décembre 2017 - 01:11:23
Document(s) archivé(s) le : samedi 17 février 2018 - 15:07:27
Jacek Kabziński. One-Dimensional Linear Local Prototypes for Effective Selection of Neuro-Fuzzy Sugeno Model Initial Structure. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.62-69, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_11〉. 〈hal-01060651〉
