Applying a Higher Number of Output Membership Functions to Enhance the Precision of a Fuzzy System

The fuzzy logic system (FLS) for real-world issues depends on the shape and number of the fuzzy membership functions (MFs), usually defined based on the relevant expert's subjective knowledge or intuition. To minimize trial-and-error and reliance on expert knowledge, the fuzzy system (FS) implementing accurate input/output MFs is proposed to increase the robustness, accuracy, and efficiency of three inputs and one output (TIOO) FS. It proposes the most appropriate relationship between input/output MFs to assign a similar input MF and a higher number of output MFs in the discourse. To evaluate the proposed technique's effectiveness, various TIOO cases have been tested in different nonlinear processes. A comparison of the outcomes of the experimental setup of the similar and higher number of output MFs performed under identical circumstances, and these outcomes are in benign contract with simulation outcomes. It is not feasible to implement modern-day optimization algorithms utilizing commercially available microcontrollers. The root mean square error (RMSE) is abridged by 45.2%, and the relative error has been decreased to an adequate range (≤±10%). FLS reduces RMSE through a higher number of distributed output MF, which improves the FS's robustness and control accuracy.