Title:GENFIS: Towards an Effective Localist NetworkAuthor(s):Mahardhika Pratama, Anavatti Sreenatha, Edwin LughoferAbstract: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.Journal:IEEE Transactions on Fuzzy SystemsISSN:1941-0034Page Reference:page 547-562, 16 page(s)Publishing:6/2014Volume:22Number:3