A Dynamic Evolving Fuzzy System for Streaming Data Prediction

This paper proposes a dynamic evolving fuzzy system (DEFS) for streaming data prediction. DEFS utilises the enhanced data potential and prediction errors of individual local models as the main criteria for fuzzy rule generation. A vital feature of the proposed system is its novel rule merging scheme that can self-adjust its tolerance towards the degree of similarity between two similar fuzzy rules according to the size of the rule base. To better handle the shifts and drifts in the data patterns, a novel rule quality measure based on both the utility values and the prediction accuracy of individual fuzzy rules is further introduced to help DEFS identify these less activated fuzzy rules with poorer descriptive capabilities and, thereby, maintaining a healthier fuzzy rule base by removing these stale rules. Very importantly, the thresholds used by DEFS are self-adaptive towards the input data. The adaptive thresholds can help DEFS to precisely capture the underlying structure and dynamically changing patterns of streaming data, enabling the system performing accurate approximation reasoning. Numerical examples based on several popular benchmark problems show the superior performance of DEFS over the state-of-the-art evolving fuzzy systems. The prediction performance of the proposed method is at least 2.88% better than the best-performing comparative EFSs on each individual regression benchmark problem considered in this study, and the average performance improvement across all the numerical experiments is approximately 30%.