Fuzzy Information and Engineering

Verma, Rajkumar; Sharma, Bhu Dev

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0137-1

AbstractHesitant fuzzy sets are considered to be the way to characterize vague phenomenon. Their study has opened a new area of research and applications. Set operations on them lead to a number of properties of these sets which are not evident in classical (crisp) sets make the area mathematically also very productive. Since these sets are defined in terms of functions and set of functions, which is not the case when the sets are crisp, it is possible to define several set operations. Such a study enriches the use of these sets. In this paper, four new operations are envisaged, defined and taken up to study a score of new identities on hesitant fuzzy sets.

Kheirfam, B.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0141-5

AbstractIn this paper, the concept of second order convexity of fuzzy mappings with fuzzy order relation under ranking function is used to formulate a second order duality for fuzzy mathematical programming problems with fuzzy coefficients, where all the fuzzy mappings are twice differentiable. Also, Wolfe's dual of these problems under ranking function is investigated, and finally, an advantage of second order duality with respect to first order duality is stated.

Bakhshi, Mahmood

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0139-z

AbstractIn this paper, given a non-commutative residuated lattice , a topological space is constructed using certain fuzzy subsets of . Indeed, we show that the set of all prime fuzzy filters of a non-commutative residuated lattice forms a topological space. Particularly, we show that this space is compact and a T0-space and its certain subspaces are Hausdorff spaces. Finally, we show that the set of all prime filters of is also a Hausdorff space.

Ikuta, A.; Siddique, N. H.; Orimoto, H.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0142-4

AbstractThe traditional standard stochastic system models, such as the autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) models, usually assume the Gaussian property for the fluctuation distribution, and the well-known least squares method is applied on the basis of only the linear correlation data. In the actual sound environment system, the stochastic process exhibits various non-Gaussian distributions, and there exist potentially various nonlinear correlations in addition to the linear correlation between input and output time series. Consequently, the system input and output relationship in the actual phenomenon cannot be represented by a simple model. In this study, a prediction method of output response probability for sound environment systems is derived by introducing a correction method based on the stochastic regression and fuzzy inference for simplified standard system models. The proposed method is applied to the actual data in a sound environment system, and the practical usefulness is verified.

Samanta, Sovan; Pal, Madhumangal

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0140-6

AbstractFuzzy competition graph as a generalization of competition graph is introduced here. Two generalizations of fuzzy competition graph as fuzzy k-competition graphs and p-competition fuzzy graphs are also defined. These graphs are related to fuzzy digraphs. Fuzzy neighbourhood graphs, related to fuzzy graphs, are also defined here. Besides, some relations between fuzzy competition graphs and fuzzy neighbourhood graphs have been established. And finally, several results to find strong edges of the above mentioned graphs have also been established.

Behera, D.; Chakraverty, S.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0138-0

AbstractThis paper presents a new and simple method to solve fuzzy real system of linear equations by solving two n x n crisp systems of linear equations. In an original system, the coefficient matrix is considered as real crisp, whereas an unknown variable vector and right hand side vector are considered as fuzzy. The general system is initially solved by adding and subtracting the left and right bounds of the vectors respectively. Then obtained solutions are used to get a final solution of the original system. The proposed method is used to solve five example problems. The results obtained are also compared with the known solutions and found to be in good agreement with them.

Georgescu, Irina

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0136-2

AbstractThe coinsurance problem is an important topic in insurance decisions. A risk-averse agent should choose a coinsurance rate maximizing the expected final wealth. In this paper, we propose a possibilistic model of coinsurance problem. A decision problem whose solution is the optimal coinsurance is formulated. Some of its properties, the calculation modality and its behavior towards the changes of risk aversion are studied.

Dutta, T. K.; Kar, S.; Purkait, S.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0143-3

AbstractIn this paper, we characterize k-regularity of semirings and study some important properties of k-regularity of semirings in terms of interval-valued fuzzy k-ideals of semirings.