The use of fuzzy linear regression for the selection of the most appropriate fuzzy implication in a fly ash-based concrete model

Abstract In this research, fuzzy linear regression (FLR) method combined with three well-known implications was implemented for evaluating the relation among amount of fly ash in concrete mixture and compressive strength concrete. More specifically, 267 experimental data 40 which were used testing validation process subjected to FLR calculating truth values, indicated degree how outputs belong predicted ones. Also, fuzziness calculated performing sensitivity analysis model. The values that emerged applying basic such as Lukasiewicz, Reinchenbach, Kleene-Dienes implication. By comparing results every implication, it concluded Lukasiewicz most appropriate implication yielded smallest deviation ( σ = 4.00) contrast theoretical ones 4.83 Reinchenbach 12.31 implication). accuracy also validated coefficient mean absolute percentage error level (MAPE 5.56%) blind prediction process, revealed application is suitable order be implications. Thus, a satisfactory procedure making inferences between parameters.