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.
https://hal.inria.fr/hal-01060651
Contributor : Hal Ifip <>
Submitted on : Thursday, November 16, 2017 - 3:47:17 PM Last modification on : Thursday, March 5, 2020 - 5:43:09 PM Long-term archiving on: : Saturday, February 17, 2018 - 3:07:27 PM
Jacek Kabziński. One-Dimensional Linear Local Prototypes for Effective Selection of Neuro-Fuzzy Sugeno Model Initial Structure. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. pp.62-69, ⟨10.1007/978-3-642-16239-8_11⟩. ⟨hal-01060651⟩