Fuzzy Information and Engineering

Cruz, Anderson; dos Santos, André V.; Santiago, Regivan H. N.; Bedregal, Benjamin

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1915455

The BDI logic is an important and widely used theoretical apparatus to represent and reason about rational agents. However, the BDI logics are incomplete regarding the intention reconsideration, override of intention, the deliberation process, and belief revision. These are essential processes of the BDI model. Also, some rational agents, especially human being, have not the approximate reasoning well represented by the BDI logics. So, in this paper, we define fuzzy semantics for a BDI language that is capable to eliminate those limitations. Additionally, we show how this paper is related to current works about BDI agents and discuss how those limitations can be fixed through the extension of the BDI logic to a fuzzy BDI logic.

Hadroug, Nadji; Hafaifa, Ahmed; Iratni, Abdelhamid; Guemana, Mouloud

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1915451

Recently, the development of the industry requires monitoring and follow-up of the working conditions of the facilities, to determine the reliability, availability, and durability of these systems, for objectively estimating the service life of these installations with reduced maintenance costs. In this sense, this work proposes a novel approach to reliability modeling, to determine failure assessment indicators based on an adaptive neuro-fuzzy inference system applied on a gas turbine. This is in order to describe the behavior of this rotating machine and to estimate their operating safety parameters, to improve its performance in terms of maintainability, availability, and operational safety with effective durability. The application of fuzzy rules to reliability estimation with practical implementations is innovative, making it possible to provide solutions to problems of reliable identification of gas turbines in their complex operating environments.

Das, Sapan Kumar; Edalatpanah, S. A.; Mandal, T.

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1915553

Purpose: We formulate a linear fractional programming (LFP) problem in which costs of the objective functions and constraints all are taken to be triangular fuzzy numbers. Methodology: The fuzzy LFP problem is transformed into an equivalent crisp line fractional programming (CLFP) problem by using the centroid ranking function. This proposed method is based on crisp LFP and has a simple structure. Findings: To show the efficiency of our proposed method a real life problem has been illustrated. The discussion of the practical problem will help decision makers to realise the usefulness of the CLFP problem. Value: Using centroid ranking function, we overcome the all limitations of our day to day real life problem. Finally, a result analysis is also established for applicability of our method.

Tiwari, S. P.; Singh, Anand P.; Pandey, Saumya

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1921376

In this note, we show that, in the paper [Zhong Y, Yan CH. Intuitionistic L-fuzzy rough sets, intuitionistic L-fuzzy preorders and intuitionistic L-fuzzy topologies. Fuzzy Inf Eng. 2016;8:255–279.], the conclusion regarding Proposition 3.3 of [Tiwari SP, Srivastava AK. Fuzzy rough sets, fuzzy preorders and fuzzy topologies. Fuzzy Sets Syst. 2013;210:63–68.] is not correct.

Rezaee, Alireza

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1921377

In this paper, a novel adaptive median filter, called the partition fuzzy is proposed. The proposed filter achieves its effect through a summation of the weighted output of the median filter and the related weighted input signal. The weights are set in accordance with the fuzzy rules. In order to design this weight function, a method to partition of observation vector space and a learning approach are proposed so that the mean square error of the filter output can be minimum. Based on constrained least mean square algorithm, an iterative learning procedure is derived and its convergence property is investigated. As details, extensive experimental results demonstrate that the proposed filter outperforms the other median-based filters in literature.

Uma, R.; Sriram, S.; Murugadas, P.

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1923621

In this paper, two types of fuzzy neutrosophic soft matrix have been introduced and it is shown that the set of all fuzzy neutrosophic soft matrices form a semiring. Further it is explored that the set of all fuzzy neutrosophic soft matrices of both types form a vector space.

Sharma, Ram Parkash; Dadhwal, Madhu; Sharma, Richa; Kar, S.

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1950390

Observing that every k-irreducible ideal of a semiring R is a k-primary ideal, if R is an additively cancellative, yoked and commutative Noetherian semiring, we establish the primary decomposition and uniqueness of the primary decomposition of k-ideals of such semirings. Finally, the primary decomposition and uniqueness of primary decomposition proved for k-ideals is also generalised for fuzzy k-ideals of these semirings.

Yan, Conghua

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1943876

In this paper, the completion of fuzzy normed linear space (in the sense of Bag and Samanta) is studied. First, some properties of convergence fuzzy point sequences are discussed. Specially, we give another characterisation of Q-neighbourhood base of for I-topology introduced by Saheli. Then we show that each fuzzy normed linear space has an (up to isomorphism) unique complete fuzzy normed linear space which contains an uniformly dense in every stratum subspace isomorphic to it.

Nasseri, S. H.; Verdegay, J. L.; Mahmoudi, F.

2021 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1906154

Recently fuzzy interval flexible linear programs have attracted many interests. These models are an extension of the classical linear programming which deal with crisp parameters. However, in most of the real-world applications, the nature of the parameters of the decision-making problems is generally imprecise. Such uncertainties can lead to increased complexities in the related optimisation efforts. Simply ignoring these uncertainties is considered undesired as it may result in inferior or wrong decisions. Therefore, inexact linear programming methods are desired under uncertainty. In this paper, we concentrate a fuzzy flexible linear programming model with flexible constraints and the interval objective function and then propose a new solving approach based on solving an associated multi-objective model. Finally, numerical example is included to illustrate the mentioned solving process.