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

TLDR

The paper proposes DENFIS, a dynamic evolving neural‑fuzzy inference system designed for adaptive online and offline learning and applied to dynamic time‑series prediction. DENFIS evolves by incremental hybrid supervised/unsupervised learning, creating and updating fuzzy rules on the fly, selecting the most activated rules for inference, and employing an evolving clustering method to support both online and offline first‑order Takagi‑Sugeno rule sets. Experiments show that DENFIS learns complex temporal sequences adaptively and outperforms several well‑known existing models.

Abstract

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 time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.