Mahardhika Pratama, Anavatti Sreenatha, Edwin Lughofer,
"GENFIS: Towards an Effective Localist Network"
, in IEEE Transactions on Fuzzy Systems, Vol. 22, Nummer 3, Seite(n) 547-562, 6-2014, ISSN: 1941-0034
Original Titel:
GENFIS: Towards an Effective Localist Network
Sprache des Titels:
Englisch
Original Kurzfassung:
Nowadays, there is an increasing demand of an
integrated system usable to real-time environments under limited
computational resources and minimum operator supervision. In
contrast, the model is also supposed to actualize high predictive
quality in order to confirm the process safety and attractive
working framework allowing the user to grasp how the particular
task is settled. A holistic concept of a fully data-driven modeling
tool namely Generic Evolving Neuro-Fuzzy Inference System
(GENEFIS) is proposed in this paper. The major spotlight of
GENEFIS is in delivering the sensible trade-off between high
predictive accuracy and parsimonious rule base while reckoning
tractable rule semantics. The viability of GENEFIS is numerically
validated via the series of experimentations using real world and
artificial datasets and is compared against state of the art of the
Evolving Neuro-Fuzzy Systems (ENFSs) where GENEFIS not
only showcases higher predictive accuracies but also lands on
more frugal structures than other algorithms.
Forschungs-