Title :
Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms
Author :
Yevgeniy Bodyanskiy;Galina Setlak;Dmytro Peleshko;Olena Vynokurova
Author_Institution :
Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, Lenina av. 14, Kharkiv, Ukraine
Abstract :
In this paper we propose architecture of hybrid generalized additive neuro-fuzzy system. Such system is hybrid of the neuro-fuzzy system of Wang-Mendel and the generalized additive models of Hastie-Tibshirani. Proposed hybrid generalized additive neuro-fuzzy system can be used for solving different tasks of computational intelligence and data stream mining. The results of experimental modelling confirm the effectiveness and computational simplicity of the proposed approach in comparison with conventional neuro-fuzzy systems.
Keywords :
"Additives","Neurons","Data mining","Tuning","Computer architecture","Fuzzy systems","Approximation methods"
Conference_Titel :
Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015 IEEE 8th International Conference on
Print_ISBN :
978-1-4673-8359-2
DOI :
10.1109/IDAACS.2015.7340753
