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.
['Yevgeniy Bodyanskiy', 'Galina Setlak', 'Dmytro Peleshko', 'Olena Vynokurova']
Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms