Deep Evolving Stacking Convex Cascade Neo-Fuzzy Network and Its Rapid Learning
Journal Title: Annals of Computer Science and Information Systems - Year 2018, Vol 15, Issue
Abstract
A deep evolving stacking convex neo-fuzzy network is proposed. It is a feedforward cascade hybrid system, the layers-stacks of which are formed by generalized neo-fuzzy neurons that implement Wang--Mendel fuzzy reasoning. The optimal in the sense of speed algorithms are proposed for its learning. Due to independent layer adjustment, parallelization of calculations in non-linear synapses and optimization of learning processes, the proposed network has high speed that allows to process information in online mode.
Authors and Affiliations
Yevgeniy Bodyanskiy, Galina Setlak, Olena Vynokurova, Iryna Pliss
Keywords
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- EP ID EP569788
- DOI 10.15439/2018F200
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