Inverse Fuzzy Research Articles

Inverse Fuzzy Fault Models for Fault Isolation and Severity Estimation in Industrial Pneumatic Valves

Inverse Fuzzy Fault Models for Fault Isolation and Severity Estimation in Industrial Pneumatic Valves

Inverse Fuzzy Mixed Planar Graphs with Application

Inverse Fuzzy Mixed Planar Graphs with Application

Production planning and control of a pharmaceutical supply chain using evolutionary fuzzy approach

This study aims to enhance the effectiveness of planning a pharmaceutical or healthcare supply chain (PHSC). Past research mostly applied simulation or optimization methods, which were time-consuming or difficult to obtain the optimal solution. This study proposes an evolutionary fuzzy approach from a multiple-criteria decision-making (MCDM) perspective to addresses this issue. Primary factors considered in the proposed methodology include product quality, capacity, availability, experience and relationship, and product cost. In the proposed methodology, a calibrated fuzzy inverse of column sum (cFICSM) method is devised to derive the fuzzy priorities of criteria. Fuzzy principal component analysis (FPCA) is proposed to resolve the dependencies among criteria. Fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) is then applied to evaluate and compare PHSC partners. Finally, the required pieces are distributed among PHSC partners. The proposed methodology has been applied to plan a PHSC for medical masks in central Taiwan during the COVID-19 pandemic. Based on the experimental results, the proposed approach minimizes the makespan while increasing the compliance with customers’ preferences by 29 %. This study concludes that planning a PHSC from an MCDM perspective can achieve synergistic effects.

Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan.

Adaptive Fuzzy Inverse Optimal Control of Nonlinear Switched Systems

Existing approaches to optimal control for uncertain switched systems require heavy computations for approximating and learning the optimal solution of the Hamilton—Jacobi—Bellman equation. To overcome this problem, a fuzzy adaptive inverse optimal control strategy is first developed for switched systems, which minimizes the cost functional but circumvents solving the Hamilton—Jacobi—Bellman equation. Specifically, an alternative practical inverse approach is developed by two Lyapunov functions method, based on which the inverse optimality regarding a meaningful cost function is achieved. In addition, a new condition of admissible edge-dependent average dwell time is developed by applying the extended multiple Lyapunov functions method. Guided by this weaker condition, the stability of the considered switched system is proved. Finally, simulations are carried out to verify the developed method.

Fuzzy Inverse Logic Method and Its Usage in the Design of Steel Truss System Elements Constructed with Steel Pipe Profiles

Bu çalışmada, yapay zekâ yöntemlerinden biri olan bulanık mantık yöntemi kullanılarak, çelik boru profillerin çekme ve basınç kuvveti etkisindeki kapasitelerini belirleyebilmek için iki adet bulanık model oluşturulmuştur. 2018 Türk Çelik yapılar Yönetmeliğinde belirtilen GKT yöntemine göre oluşturulan her iki bulanık modelde de, çelik sınıfı S355 olarak sabit olarak tutulurken, kesit çapı (D), profil et kalınlığı (t) ve eleman uzunluğu (L) değişken parametreler olarak dikkate alınmıştır. Eksenel çekme kapasitesi (Tn) ve eksenel basınç kapasitesi (Pn) ayrı ayrı olarak bu modellerin çıktı parametrelerini oluşturmuşlardır. Her iki modelin oluşturulmasında aynı girdi değişkenleri değerlerine sahip ancak çıktı parametreleri farklı olan 1400 ‘er adet örnek çözüm kullanılmıştır. Kullanılan bu örnek çözümlerin haricinde 988 ‘şer adet farklı örnek çözüm ile bu modeller test edilerek, sırasıyla maksimum % 2.764 ve maksimum % 4.076 hata ile eksenel çekme ve basınç dayanımlarının tahminde kullanılabilecekleri ortaya konulmuştur. Daha sonra geliştirilen bulanık modellere, bulanık ters mantık yöntemi 3 farklı izostatik düzlem kafes sistem örneği için uygulanarak bu kafes sistemleri oluşturan çubuk elemanların tasarımları gerçekleştirildikten sonra dayanım kontrolleri karşılaştırmalı olarak 2018 Türk Çelik yapılar Yönetmeliğinde belirtilen GKT yöntemi ile gerçekleştirilmiştir. Sonuç olarak, bulanık mantık ve bulanık ters mantık yöntemlerinin birlikte aynı sayısal veriyi kullanarak boru kesitli çelik kafes sistem elemanların kapasitelerinin belirlenmesinde ve aynı zamanda tasarımlarının gerçekleştirilmesinde model hataları da dikkate alınarak güvenle kullanılabilecekleri ortaya konulmuştur.

Fuzzy transform-based approximation method for solving fractional semi-explicit differential-algebraic equations

We present an efficient numerical method to approximate the solution of a system of fractional-order linear semi-explicit differential-algebraic equations with variable coefficients. The method is based on the use of the direct and inverse fuzzy transforms ( $${\mathcal {F}}$$ -transforms). By employing this method, we obtain an analytical approximate solution to the main problem in terms of flexible basic functions. The non-local property of fuzzy transforms helps us to have an efficient method for problems involving non-singular kernels. The error analysis and convergence evaluation of the method are demonstrated in detail. We give some examples to illustrate the significant features of the method.

An Approach to Calculate Inverse of LR-Trapezoidal Fuzzy Matrix

In this paper, we devised a numerical algorithm for calculating the inverse of trapezoidal fuzzy numbers matrix. By analysing the existence of fuzzy inverse, the sufficient conditions are restricted. Furthermore, through calculating the approximate fuzzy inverse, the algorithm can be widely used in solving fully fuzzy linear system and it is validated by some given examples.

Picture fuzzy sub-hyperspace of a hyper vector space and its application in decision making problem

<abstract><p>In this paper, the notion of picture fuzzy sub-hyperspace of a hyper vector space is introduced and some related results are investigated on the basis of some basic operations (intersection, union, Cartesian product etc.) on picture fuzzy sets. The concept of picture fuzzy linear transformation with respect to some picture fuzzy sub-hyperspace is initiated here and some important results are studied in this regard. It is shown that with respect to some pre-assumed picture fuzzy sub-hyperspace, linear combination of two picture fuzzy linear transformations is a picture fuzzy linear transformation, composition of two picture fuzzy linear transformations is a picture fuzzy linear transformation and inverse of a bijective picture fuzzy linear transformation is a picture fuzzy linear transformation. The effect of good linear transformation on picture fuzzy sub-hyperspaces is discussed here. It is shown that the image of a picture fuzzy sub-hyperspace is a picture fuzzy sub-hyperspace under bijective good linear transformation and the inverse image of a picture fuzzy sub-hyperspace is a picture fuzzy sub-hyperspace under good linear transformation. Some important results on picture fuzzy sub-hyperspaces in the light of $ (\theta, \phi, \psi) $-cut of picture fuzzy set are studied here. Finally, an application of picture fuzzy sub-hyperspace conditions in decision making problem is presented here.</p></abstract>

An intelligent system for assisting personalized COVID-19 vaccination location selection: Taiwan as an example

In many regions of the world, with the gradual increase in the supply of COVID-19 vaccines, COVID-19 vaccination has changed from centralized government control to personalized selection. When choosing a location for COVID-19 vaccination, in addition to subjective preferences, objective information (such as the expected waiting time at a COVID-19 vaccination location and the crowdedness and reliability of the vaccination location) also need to be considered. However, it is not convenient for an individual to collect and compare such information. To address this issue, this research applies web content mining to extract the conditions of COVID-19 vaccination locations. Then, a novel asymmetric calibrated fuzzy inverse of column sum and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje recommendation mechanism is proposed. Finally, an intelligent system is developed to assist a user in selecting a personalized COVID-19 vaccination location. In a regional experiment conducted in Taichung City, Taiwan, the developed intelligent system was applied to assist 20 users in choosing personalized COVID-19 vaccination locations. The successful recommendation rate was 95%.

Adaptive Tracking Design of NCS with Time-varying Signals Using Fuzzy Inverse Model

Adaptive Tracking Design of NCS with Time-varying Signals Using Fuzzy Inverse Model

A classification algorithm based on multi-dimensional fuzzy transforms

We present a new classification algorithm for machine learning numerical data based on direct and inverse fuzzy transforms. In our previous work fuzzy transforms were used for numerical attribute dependency in data analysis: the multi-dimensional inverse fuzzy transform was used to approximate the regression function. Also here the classification method presented is based on this operator. Strictly speaking, we apply the K-fold cross-validation algorithm for controlling the presence of over-fitting and for estimating the accuracy of the classification model: for each training (resp., testing) subset an iteration process evaluates the best fuzzy partitions of the inputs. Finally, a weighted mean of the multi-dimensional inverse fuzzy transforms calculated for each training subset (resp., testing) is used for data classification. We compare this algorithm on well-known datasets with other five classification methods.

Fuzzy Inverse Barrier Method to Find Primal and Dual Optimality Solutions of Fuzzy

This article highlights a fuzzy inverse barrier method with a ranking of R>0 parameters to solve problems of fuzzy linear programming. In the method, a fuzzy inverse barrier function algorithm establishes the existence of a limit to arrive at an optimal solution to a given problem. A current algorithmic method, some fuzzy inverse barrier method examples are included.

Attribute dependency data analysis for massive datasets by fuzzy transforms

We present a numerical attribute dependency method for massive datasets based on the concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts for numerical attribute dependency in data analysis: Therein, the multi-dimensional inverse fuzzy transform was useful for approximating a regression function. Here we give an extension of this method in massive datasets because the previous method could not be applied due to the high memory size. Our method is proved on a large dataset formed from 402,678 census sections of the Italian regions provided by the Italian National Statistical Institute (ISTAT) in 2011. The results of comparative tests with the well-known methods of regression, called support vector regression and multilayer perceptron, show that the proposed algorithm has comparable performance with those obtained using these two methods. Moreover, the number of parameters requested in our method is minor with respect to those of the cited in the above two algorithms.

Uncertainty-based rainfall network design using a fuzzy spatial interpolation method

The importance of optimization of rain gauge stations locations is critical given that rainfall data is central to various water-related studies. As rainfall data has vagueness in nature, fuzzy set theory can describe uncertainties existing in rainfall data. In this paper, we develop a framework to rainfall network design that combines fuzzy concepts and a deterministic spatial interpolation method known as Fuzzy Inverse Distance Weighted (FIDW). It addresses two important issues: (1) the assessment of two types of fuzzy mathematical approaches known as Fuzzy Standard IDW (FS-IDW) and Fuzzy Modified IDW (FM-IDW); (2) the comparison of the FIDW with spatial and spatiotemporal network designs using Ordinary Kriging (OK), known as OK-S and OK-ST, respectively. We consider four objective functions (OF): interval-based Estimation Error Variance Types 1 and 2 (EEVT1, EEVT2), Mean Square Error (MSE) and Coefficient of Determination (R2). Four scenarios of number of removed stations including 5, 10, 15 and 20 are also analysed via statistical indicators. Firstly, the FIDW parameters (power and radius) are optimized for each OF. Then, we resort to a Genetic Algorithm (GA) to solve these OFs. Percentage of similarity between optimal removed station in both FIDW methods (FS-IDW and FM-IDW) is higher than OK-ST method. Between FIDW methods, FM-IDW yields better results. Statistical results of four removed stations (5, 10, 15 and 20 rain gauge stations) show that the highest variation in estimation accuracy is from 5 to 20 removed stations which belongs to EEVT1 OF and is around 30%.

Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform

Periodic noise degrades the image quality by overlaying similar patterns. This noise appears as peaks in the image spectrum. In this research, a method based on fuzzy transform has been developed to identify and reduce the peaks adaptively. We convert the periodic noise removal task as image compression and a smoothing problem. We first utilize the direct and inverse fuzzy transform of the spectrum to detect periodic noise peaks. Second, we propose a fuzzy transform-based notch filter for spectral smoothing and separating the original image from the periodic noise components. This noise correction approach filters out a portion (given by fuzzy transform) of the noise component. Extensive experiments on both synthetic and non-synthetic noisy images have been carried out to validate the effectiveness and efficiency of the proposed algorithm. The simulation results demonstrate that the proposed method outperforms state of the art algorithms both visually and quantitatively.

Assessing Mobile and Smart Technology Applications for Active and Healthy Aging using a Fuzzy Collaborative Intelligence Approach

Active and healthy living is critical in an aging society. Mobile and smart technology applications can assist in achieving this goal of healthy living. However, selecting a suitable and smart technology application for active and healthy living is difficult. To address this difficulty, a fuzzy collaborative intelligence (FCI) approach was proposed in this study to assess the suitability of a mobile and smart technology application. The FCI approach is a posterior-aggregation fuzzy analytic hierarchy process approach that combines the fuzzy inverse of column sum, partial-consensus fuzzy intersection, and fuzzy technique for order preference by similarity to the ideal solution. The FCI approach starts from the prioritization of critical factors using the fuzzy inverse of column sum by each expert. Subsequently, partial-consensus fuzzy intersection is applied to aggregate the priorities derived by all experts. Based on the aggregation result, each mobile and smart technology application for active and healthy aging is assessed using fuzzy technique for order preference by similarity to the ideal solution. The FCI approach was applied to assess five existing mobile and smart technology applications, with various aspects of life as parameters. According to experimental results, the most and least suitable mobile technology applications were smart canes and online food ordering and delivery platforms, respectively. This is because the current elderly population is not very familiar with smartphone applications. This problem will be solved over time when the current middle-age population becomes old. In addition, the ranking result obtained using the proposed methodology was considerably different from those using several existing methods. The aging of population is a natural phenomenon. The results of this study are helpful to creating an environment that is friendly and assists elderly people to age actively and healthily.

A Robust Fuzzy PD Inverse Dynamics Decoupling Control of Spherical Motion Mechanism With Fuzzy Linear Extended State Observer

In this paper, a robust adaptive fuzzy proportional-derivative inverse dynamics decoupling control scheme with fuzzy-based linear extended state observer (FLESO) is presented and applied to the trajectory tracking control of a two degree-of-freedom (2-DOF) spherical motion mechanism (SMM). The dynamics of the SMM has the characteristics of multivariable nonlinearity, uncertainties and strong coupling. Uncertainties like the modeling errors and external disturbances affect the tracking performance, and coupling increases the difficulty of controller design and reduces the tracking precision. Therefore, a novel hybrid control scheme that is composed of a fuzzy proportional-derivative (FPD) feedback control with varying gains, inverse dynamic model-based feed-forward decoupling term, FLESO with varying bandwidth, and robust term is developed. The novel control strategy combines the advantages of simplicity and easy design of the FPD control, the effectiveness of the FLESO to handle the modeling errors and external disturbances, and the robustness of the robust term to estimation errors of the FLESO. First, introduce the structure of the SMM and establish the dynamic model. Second, the feed-forward decoupling principle is derived based on the inverse dynamic model. Then the FPD control with two-inputs and two-outputs is designed, whose rule base is derived by the phase plane method. The linear extended state observer is designed, whose bandwidth is tuned via the fuzzy logic system. Furthermore, the asymptotic stability of the proposed controller is proved by the Lyapunov theorem. Finally, the high tracking performance of the proposed controller is validated via both simulation and experiment results.

The Application of Fuzzy Collaborative Intelligence to Detect COVID-19 Minor Symptoms

Coronavirus Illness 2019 (COVID-19), a rare disease carried by a coronavirus known as a novel coronavirus, is now posing a danger to the whole planet. Despite the rising number of cases, there is no commercially available vaccination for COVID-19. The moderate symptoms of COVID-19 illness, on the other hand, may be treated with a variety of antiviral treatments. Even yet, selecting the optimum antiviral medication to manage the moderate symptom of COVID-19 is a difficult and ambiguous option. Selecting a drug might be challenging. Fuzzy collaborative intelligence (FCI) was presented in this research as a solution to solve the difficulty of evaluating the appropriateness of a drug selection. In the FCI method, the fuzzy inverse of column sum, partial consensus fuzzy intersection, and fuzzy procedure for order preference by similarity to the ideal solution. To show the practicality and usefulness of the created approach in real-world applications, a case study of medication choice for COVID-19 illness is being investigated.

Inverse Fuzzy Graphs with Applications

Rosenfeld [A. Rosenfeld, Fuzzy Graphs, Fuzzy Sets and Their Applications, eds. L. A. Zadeh, K. S. Fu and M. Shimura (Academic Press, New York, 1975), pp. 77–95.] defined the fuzzy relations on the fuzzy sets and developed the structure of fuzzy graph, as a graph with a membership degree (between zero and one) for the vertices and edges such that the membership degree of every edge is less than or equal to the minimum of the membership degree of its endpoints. Although this model of graph has many applications in the real life, it fails to solve a lot of problems, which we can use graph for its representation. This paper aimed to demonstrate a new type of graph with a membership degree (between zero and one) for the vertices and edges so that the membership degree of every edge becomes more than or equals the minimum of the membership degrees of its endpoints. This new type of graph is called inverse fuzzy graph “or” I-fuzzy graph, which can play a role in solving many problems which are not solved by fuzzy graph.