|
|
|
 |
Search published articles |
 |
|
Mahdieh Mozafari, Mohammad Khanjari Sadegh, , Gholamreza Hesamian,
Volume 17, Issue 1 (9-2023)
Abstract
In this paper, some reliability concepts have been investigated based on the α-pessimistic and its relationship with the α-cut of a fuzzy number. For this purpose, if the lifetime distribution of the system components is known, using the definition of the scale fuzzy random variable, based on α-pessimistic, some reliability criteria have been investigated. Also, suppose the lifetime distribution of the components is unknown or only the fuzzy observations of the lifetime of the features are available. In that case, the empirical distribution function of the fuzzy data is used to estimate the reliability, and some examples are provided to illustrate the results.
Miss. Mahdieh Mozafari, Dr. Mohammad Khanjari Sadegh, Dr. Mohammad Ghasem Akbari, Dr. Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, fuzzy order statistics are expressed based on the concept of α-value, and some of its applications in reliability have been examined. For this purpose, if the lifetime distribution of the system components is known, some of the reliability criteria of the $i$th order statistic using the definition of a fuzzy random variable based on the α-value have been investigated. Also, if the lifetime distribution of the components is unknown or only the fuzzy observations of the lifetime of the components are available, the empirical distribution function of the fuzzy data is used to estimate the reliability based on ordinal statistics, and examples are provided to illustrate the results.
Hossein Mohammadi, Mohammad Ghasem Akbari, Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
First, this article defines a meter between fuzzy numbers using the support function. Then, based on the support function, the concepts of variance, covariance, and correlation coefficient between fuzzy random variables are expressed, and their properties are investigated. Then, using the above concepts, the p-order fuzzy autoregressive model is introduced based on fuzzy random variables, and its properties are investigated. Finally, to explain the problem further, examples will be presented and compared with similar models using some goodness of fit criteria.
Ms. Samira Taheri, Dr Mohammad Ghasem Akbari, Dr Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, based on the concept of $alpha$-values of fuzzy random variables, the fuzzy moving average model of order $q$ is introduced. In this regard, first, the definitions of variance, covariance, and correlation coefficient between fuzzy random variables are presented, and their properties are investigated. In the following, while introducing the fuzzy moving average model of order $q$, this model's autocovariance and autocorrelation functions are calculated. Finally, some examples are presented for the obtained results.
Maryam Maleki, Hamid Reza Nili-Sani, M.g. Akbari,
Volume 18, Issue 2 (2-2025)
Abstract
In this paper, we consider the issue of data classification in which the response (dependent) variable is two (or multi) valued and the predictor (independent) variables are ordinary variables. The errors could be nonprecise and random. In this case, the response variable is also a fuzzy random variable. Based on this and logistic regression, we formulate a model and find the estimation of the coefficients using the least squares method. We will describe the results with an example of one independent random variable. Finally, we provide recurrence relations for the estimation of parameters. This relation can be used in machine learning and big data classification.
Jalal Chachi, Mohammadreza Akhond, Shokoufeh Ahmadi,
Volume 18, Issue 2 (2-2025)
Abstract
The Lee-Carter model is a useful dynamic stochastic model representing the evolution of central mortality rates over time. This model only considers the uncertainty about the coefficient related to the mortality trend over time but not the age-dependent coefficients. This paper proposes a fuzzy extension of the Lee-Carter model that allows quantifying the uncertainty of both kinds of parameters. The variability of the time-dependent index is modeled as a stochastic fuzzy time series. Likewise, the uncertainty of the age-dependent coefficients is quantified using triangular fuzzy numbers. Considering this last hypothesis requires developing and solving a fuzzy regression model. Once the generalization of the desired fuzzy model is introduced, we will show how to fit the logarithm of the central mortality rate in Khuzestan province using by using fuzzy numbers arithmetic during the years 1401-1383 and random fuzzy forecast in the years 1402-1406.
Elham Ranjbar, Mohamad Ghasem Akbari, Reza Zarei,
Volume 19, Issue 1 (9-2025)
Abstract
In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy autoregressive time series model based on the support vector machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the model, and the constraints included in the model have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy autoregressive time series model, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods.
|
|
