A k-nearest neighborhood based fuzzy reasoning schema

In this paper a k-nearest neighborhood based fuzzy reasoning is introduced. The proposed approach is necessary in order to estimate the firing degree of each rule for a fuzzy if-then rule base where the antecedent is an n-dimensional vector. The proposed methodology is applied to well known Box and Jenkins gas-furnace data and compared with two other algorithms. The first algorithm is the slightly modified version of the well-known Sugeno-Yasukawa (1993) methodology proposed by Turksen, Bazoon et al. (1997), and the second algorithm is the adaptive network-based fuzzy inference schema proposed by Jang (1996). The test data prediction RMSE´s obtained from the gas-furnace data is 1.03 for Turksen-Bazoon approach, 0.52 for ANFIS and 0.43 for the proposed algorithm