DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

@article{Kasabov2002DENFISDE,
  title={DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction},
  author={Nikola K. Kasabov and Qun Song},
  journal={IEEE Trans. Fuzzy Systems},
  year={2002},
  volume={10},
  pages={144-154}
}
This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each… CONTINUE READING
Highly Influential
This paper has highly influenced a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,268 citations. REVIEW CITATIONS

8 Figures & Tables

Topics

Statistics

050100'01'03'05'07'09'11'13'15'17
Citations per Year

1,268 Citations

Semantic Scholar estimates that this publication has 1,268 citations based on the available data.

See our FAQ for additional information.