A change point estimation approach for fuzzy logistic regression profiles in Phase II

Todays, the performance of a process or the quality of a product in conditions of uncertainty and under distributions from the exponential distribution family is evaluated by a fuzzy communication model with binary data called fuzzy generalized linear profiles. Generalized linear profiles are a type of nonlinear profile in which process observations follow the Bernoulli or binomial distribution. In this research, approaches In order to monitor fuzzy generalized linear profiles in phase 2, we propose. The main purpose of this paper is to monitor the fuzzy statistical process to detect the time of occurrence of changes in processes as fuzzy change point and based on the principle of maximum likelihood (MLE). Is based on fuzzy observations. Performance of the proposed method for monitoring fuzzy generalized linear profiles based on the probability of an out-of-control signal using the fuzzy control diagram (FEWMA) and then estimating the fuzzy change point for the simulated data and real data.