IT2-GSETSK: An evolving interval Type-Ⅱ TSK fuzzy neural system for online modeling of noisy data

摘要

As a core part of a fuzzy neural system, the rule base antecedents and consequents may carry uncer-tainties because they are trained using noisy data. So, handling the uncertain rule base is an important need in some specific problems such as noisy non-dynamic problems which leads a better data model-ing. As a solution, Interval Type-II (IT2) version of GSETSK (Generic Self-Evolving Takagi-Sugeno-Kang), namely IT2-GSETSK, is presented in this paper. This solution uses IT2 membership functions for handling uncertainties, plus having Type-I (GSETSK) capabilities. In this way IT2-GSETSK is a fully-online model able to handle data streams and cope with time-variant data. It also provides up-to-date, relevant and compact rule base that is easily interpretable by human. The IT2-GSETSK is tested over several applica-tions including medical, environmental and financial predictions, which show satisfactory performance of IT2-GSETSK. Moreover, it is observed that while GSETSK performs well enough for dynamic problems with less noise, noisy non-dynamic problems benefit significantly from IT2-GSETSK. (C) 2020 Elsevier B.V. All rights reserved.