توسعه رویکرد تخمین نقطه تغییر در پروفایل‌های رگرسیون لجستیک فازی در فاز 2

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه سمنان، سمنان، ایران

چکیده

چکیده: امروزه عملکرد یک فرآیند یا کیفیت یک محصول در شرایط عدم اطمینان و تحت توزیع­هایی از خانواده توزیع نمایی توسط یک مدل ارتباطی فازی با داده­های دودویی با عنوان پروفایل­های خطی تعمیم­یافته فازی ارزیابی می­شوند. پروفایل­های خطی تعمیم یافته یکی از انواع پروفایل­های غیر خطی است که در آن مشاهدات فرآیند از توزیع غیر نرمال برنولی یا دوجمله­ای پیروی می­کنند. در این تحقیق رویکردهایی به منظور پایش پروفایل­های خطی تعمیم­یافته فازی در فاز 2 پیشنهاد می­کنیم. هدف اصلی این نوشتار پایش فرآیند آماری فازی برای کشف زمان وقوع تغییر در فرآیندها تحت عنوان نقطه تغییر فازی می­باشد و بر اساس اصل حداکثر درستنمایی (MLE) برای مشاهدات فازی استوار است. عملکرد روش پیشنهادی به منظور پایش پروفایل­های خطی تعمیم­یافته فازی و بر اساس احتمال یک سیگنال خارج از کنترل با استفاده از نمودار کنترل فازی (FEWMA) و سپس برآورد نقطه تغییر فازی برای داده­های شبیه­سازی شده و یک مثال عددی برای کارایی روش پیشنهادی نشان داده می­شود.

کلیدواژه‌ها


عنوان مقاله [English]

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

نویسندگان [English]

  • Mona Gharegozloo
  • Reza Kamranrad
Department of Industrial Engineering, Faculty of engineering, Semnan university, Semnan, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Profile
  • fuzzy logistic regression
  • maximum probability estimator
  • fuzzy change point
  • Phase II
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