MISO hierarchical inference engine satisfying the law of importation with aggregation functions
This work addresses computational bottlenecks in fuzzy systems for applications like control or decision-making, but it appears incremental as it builds on existing fuzzy logic theory.
The paper tackled the computational efficiency of multi-input-single-output fuzzy inference engines by investigating three hierarchical engines based on fuzzy implications that satisfy the law of importation with aggregation functions, resulting in theoretical characterizations and constructions of these engines.
Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In order to enhance the computational efficiency of fuzzy inference engine in multi-input-single-output(MISO) fuzzy systems,this paper aims mainly to investigate three MISO fuzzy hierarchial inference engines based on fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy (LIA). For a given aggregation function, the fuzzy implication which satisfies (LIA) with this aggregation function is then characterized. Finally, we construct three fuzzy hierarchical inference engines in MISO fuzzy systems applying aforementioned theoretical developments.