scholarly journals Computing the Number of Failures for Fuzzy Weibull Hazard Function

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2858
Author(s):  
Hennie Husniah ◽  
Asep K. Supriatna
Keyword(s):  
Hazard Function ◽  
Manufacturing System ◽  
Shape Parameter ◽  
Fuzzy Numbers ◽  
Mean Value ◽  
Triangular Fuzzy Number ◽  
Weighting Factor ◽  
Probabilistic Uncertainty ◽  
Α Level ◽  
Parameter Approach

The number of failures plays an important factor in the study of maintenance strategy of a manufacturing system. In the real situation, this number is often affected by some uncertainties. Many of the uncertainties fall into the possibilistic uncertainty, which are different from the probabilistic uncertainty. This uncertainty is commonly modeled by applying the fuzzy theoretical framework. This paper aims to compute the number of failures for a system which has Weibull failure distribution with a fuzzy shape parameter. In this case two different approaches are used to calculate the number. In the first approach, the fuzziness membership of the shape parameter propagates to the number of failures so that they have exactly the same values of the membership. While in the second approach, the membership is computed through the α-cut or α-level of the shape parameter approach in the computation of the formula for the number of failures. Without loss of generality, we use the Triangular Fuzzy Number (TFN) for the Weibull shape parameter. We show that both methods have succeeded in computing the number of failures for the system under investigation. Both methods show that when we consider the function of the number of failures as a function of time then the uncertainty (the fuzziness) of the resulting number of failures becomes larger and larger as the time increases. By using the first method, the resulting number of failures has a TFN form. Meanwhile, the resulting number of failures from the second method does not necessarily have a TFN form, but a TFN-like form. Some comparisons between these two methods are presented using the Generalized Mean Value Defuzzification (GMVD) method. The results show that for certain weighting factor of the GMVD, the cores of these fuzzy numbers of failures are identical.

scholarly journals Soil water management problems using fuzzy arithmetic

2013 ◽  
Vol 11 (4) ◽  
pp. 556-565
Keyword(s):  
Hydraulic Conductivity ◽  
Uncertain Data ◽  
Triangular Fuzzy Number ◽  
Effective Porosity ◽  
Specific Yield ◽  
Fuzzy Arithmetic ◽  
Two Dimensional ◽  
Fuzzy Analysis ◽  
Management Problems ◽  
Α Level

Drainage management problems are usually very hard to simulate due to the uncertainty of the hydraulic parameters involved. Fuzzy analysis is one of the available tools that can be used for such problems, involving uncertain data. A fuzzy analysis approach usually involves the consideration of several α-level cuts and an analytical approach or an explicit scheme approach for the PDE's discretization. Several application examples of this approach are listed in the literature, including uncertainty in hydraulic conductivity, specific yield, transmissivities, porosities, dispersivities, and deoxygenation rate coefficient. A methodology for the simulation of drainage problem having vague values of hydraulic parameters is introduced in this paper, and an analytical solution for a two-dimensional drainage application is presented. The two-dimensional problem of drainage is handled using fuzzy analysis by defining the hydraulic conductivity K as a triangular fuzzy number (TFN). The method of interval analysis is used in all the α-level cut examples. A solution is obtained using eleven α-level cuts and also solutions for two, three, and five α-level cuts are presented. Trials for different values of effective porosity are also performed. Finally conclusions on the necessary number of α-cuts utilized for drainage problems are drawn.

A MODIFICATION OF THE INDEX OF LIOU AND WANG FOR RANKING FUZZY NUMBER

2007 ◽  
Vol 15 (04) ◽  
pp. 411-424 ◽  
Author(s):  
M. SOCORRO GARCIA ◽  
M. TERESA LAMATA
Keyword(s):  
Satisfactory Result ◽  
Coefficient Of Variation ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Mean Value ◽  
Weighted Mean ◽  
Numerical Examples ◽  
Centroid Point ◽  
Ranking Fuzzy Number ◽  
Index Of Optimism

Different methods have been proposed for ranking fuzzy numbers. These include methods based on distances, centroid point, coefficient of variation, and weighted mean value. However, there is still no method that can always give a satisfactory result to every situation; some are counterintuitive and not discriminating. This paper presents an approach for ranking fuzzy numbers with integral value that is an extension of the index of Liou and Wang. This method, that is independent of the type of membership function used, can rank more than two fuzzy numbers simultaneously. This ranking method use an index of optimism to reflect the decision maker's optimistic attitude, but rather it also contains an index of modality that represents the neutrality of the decision maker. The approach is illustrated with numerical examples.

A new evaluation of mean value for fuzzy numbers and its application to American put option under uncertainty

2006 ◽  
Vol 157 (19) ◽  
pp. 2614-2626 ◽  
Author(s):  
Yuji Yoshida ◽  
Masami Yasuda ◽  
Jun-ichi Nakagami ◽  
Masami Kurano
Keyword(s):  
Fuzzy Numbers ◽  
Mean Value ◽  
American Put Option ◽  
Put Option ◽  
American Put

scholarly journals New Similarity of Triangular Fuzzy Number and Its Application

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xixiang Zhang ◽  
Weimin Ma ◽  
Liping Chen
Keyword(s):  
Collaborative Filtering ◽  
Fuzzy Number ◽  
Recommendation System ◽  
Fuzzy Numbers ◽  
Comprehensive Evaluation ◽  
Cloud Model ◽  
Triangular Fuzzy Number ◽  
New Method ◽  
Triangular Fuzzy Numbers

The similarity of triangular fuzzy numbers is an important metric for application of it. There exist several approaches to measure similarity of triangular fuzzy numbers. However, some of them are opt to be large. To make the similarity well distributed, a new method SIAM (Shape’s Indifferent Area and Midpoint) to measure triangular fuzzy number is put forward, which takes the shape’s indifferent area and midpoint of two triangular fuzzy numbers into consideration. Comparison with other similarity measurements shows the effectiveness of the proposed method. Then, it is applied to collaborative filtering recommendation to measure users’ similarity. A collaborative filtering case is used to illustrate users’ similarity based on cloud model and triangular fuzzy number; the result indicates that users’ similarity based on triangular fuzzy number can obtain better discrimination. Finally, a simulated collaborative filtering recommendation system is developed which uses cloud model and triangular fuzzy number to express users’ comprehensive evaluation on items, and result shows that the accuracy of collaborative filtering recommendation based on triangular fuzzy number is higher.

scholarly journals A Novel Approach to Solve Fully Fuzzy Linear Programming Problems with Modified Triangular Fuzzy Numbers

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2937
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Elnaz Osgooei ◽  
Gholamreza Haseli ◽  
Hana Tomaskova
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Triangular Fuzzy Number ◽  
Fuzzy Linear Programming ◽  
Fuzzy Decision ◽  
Triangular Fuzzy Numbers ◽  
Novel Approach ◽  
Decision Variables ◽  
Linear Programming Problems ◽  
Fuzzy Solution

Recently, new methods have been recommended to solve fully fuzzy linear programming (FFLP) issues. Likewise, the present study examines a new approach to solve FFLP issues through fuzzy decision parameters and variables using triangular fuzzy numbers. The strategy, which is based on alpha-cut theory and modified triangular fuzzy numbers, is suggested to obtain the optimal fully fuzzy solution for real-world problems. In this method, the problem is considered as a fully fuzzy problem and then is solved by applying the new definition presented for the triangular fuzzy number to optimize decision variables and the objective function. Several numerical examples are solved to illustrate the above method.

scholarly journals Computer-Based Fuzzy Numerical Method for Solving Engineering and Real-World Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Naila Rafiq ◽  
Naveed Yaqoob ◽  
Nasreen Kausar ◽  
Mudassir Shams ◽  
Nazir Ahmad Mir ◽  
...  
Keyword(s):  
Fuzzy Sets ◽  
Numerical Method ◽  
Nonlinear Equations ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Numerical Solutions ◽  
Numerical Test ◽  
Real Life ◽  
Triangular Fuzzy Number ◽  
Test Problems

The nonlinear equation is a fundamentally important area of study in mathematics, and the numerical solutions of the nonlinear equations are also an important part of it. Fuzzy sets introduced by Zedeh are an extension of classical sets, which have several applications in engineering, medicine, economics, finance, artificial intelligence, decision-making, and so on. The most special types of fuzzy sets are fuzzy numbers. The important fuzzy numbers are trapezoidal fuzzy and triangular fuzzy numbers, which have several applications. In this research article, we propose an efficient numerical iterative method for estimating roots of fuzzy nonlinear equations, which are based on the special type of fuzzy number called triangular fuzzy number. Convergence analysis proves that the order of convergence of the numerical method is three. Some real-life applications are considered as numerical test problems from engineering, which contain fuzzy quantities in the parametric form. Engineering models include fractional conversion of nitrogen-hydrogen feed into ammonia and Van der Waal’s equation for calculating the volume and pressure of a gas and motion of the object under constant force of gravity. Numerical illustrations are given to show the dominance efficiency of the newly constructed iterative schemes as compared to existing methods in the literature.

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