UTILIZING THE INVERSE WEIBULL DISTRIBUTION FOR AGRICULTURAL DATA ANALYSIS WITH FUZZY INTERVAL PARAMETERS

In several scientific fields, the inverse Weibull distribution (IWD) is widely used as a model for dependability analysis. This paper's main objective is to develop a linear, exponential loss function based on lowest record values for the Inverse Weibull Distribution's dependability value and variables. After weighting the LXLF in the construction of the current function loss, the modified linear, exponential loss function (WLXLF) was given its name. Next, estimate the Inverse Weibull distribution's parameters and reliability functions using WLXLF. This paper uses real-time data to estimate the parameter intervals for the inverse Weibull distribution using two estimation methods. The maximum likelihood approach and the relative maximum likelihood method are the foundations of these two tactics. Additionally, we assess how relative maximum likelihood intervals for actual datasets compare to maximum likelihood intervals. Consequently, determining the values of the IWD's maximum likelihood intervals using fuzzy parameters inside the Corresponding real-time problem's interval. Using a fuzzy range of parameter values for real-time data, the present study investigated the probability density function, cumulative density function, and hazard rate function of the inverse Weibull distribution. We examined Tamil Nadu's agricultural output in 2018 in this research. It was thus shown from the comparison that the fuzziness interval estimate performs better than the real-time data.