Anytime Extension of Generalized Fuzzy Neural Network Models with Non-singleton Consequents

Author:

Várkonyi-Kóczy Annamária R.

Publisher

Springer Berlin Heidelberg

Reference17 articles.

1. Baranyi, P., Lei, K., Yam, Y.: Complexity reduction of singleton based neuro-fuzzy algorithm. In: Proc. of the 2000 IEEE International Conference on Systems, Man, and Cybernetics, Nashville, USA, vol. 4, pp. 2503–2508 (2000)

2. Baranyi, P., Várkonyi-Kóczy, A.R., Yam, Y., Michelberger, P.: HOSVD based Computational Complexity Reduction of TS Fuzzy Models. In: Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, IFSA / NAFIPS 2001, Vancouver, Canada, pp. 2482–2485 (2001)

3. Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice Hall Int. Inc., USA (1988)

4. Lovassy, R., Kóczy, L.T., Gál, L.: Function Approximation Performance of Fuzzy Neural Networks. Acta Polytechnica Hungarica 7(4), 25–38 (2010)

5. Studies in Computational Intelligence;L. Madarász,2009

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1. A Survey of Usage of Anytime Algorithm in Fault detection in Cloud Systems;2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI);2023-01-19

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