Fuzzy Information and Engineering

Eftekhari, M.; Zeinalkhani, M.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0144-2

AbstractThis paper exploits the ability of a novel ant colony optimization algorithm called gradient-based continuous ant colony optimization, an evolutionary methodology, to extract interpretable first-order fuzzy Sugeno models for nonlinear system identification. The proposed method considers all objectives of system identification task, namely accuracy, interpretability, compactness and validity conditions. First, an initial structure of model is obtained by means of subtractive clustering. Then, an iterative two-step algorithm is employed to produce a simplified fuzzy model in terms of number of fuzzy sets and rules. In the first step, the parameters of the model are adjusted by utilizing the gradient-based continuous ant colony optimization. In the second step, the similar membership functions of an obtained model merge. The results obtained on three case studies illustrate the applicability of the proposed method to extract accurate and interpretable fuzzy models for nonlinear system identification.

Abo-Tabl, E. A.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0149-x

AbstractThis paper proposes new definitions of lower and upper mset approximations, which are basic concepts of the rough mset theory. These definitions come naturally from the concepts of multiset topologies and of ambiguity introduced in this paper. The new definitions are compared to classical definitions and are shown to be more general. In the sense, they are the only ones which can be used for any type of indiscernibility or similarity mset relation.

Nasseri, S. H.; Behmanesh, E.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0151-3

AbstractThe objective of this paper is to deal with a kind of fuzzy linear programming problem involving triangular fuzzy numbers. Then some interesting and fundamental results are achieved which in turn lead to a solution of fuzzy linear programming models without converting the problems to the crisp linear programming models. Finally, the theoretical results are also supported by a real case study in a banking system. The same idea is emphasized to be also useful when a general LR fuzzy numbers is given.

Ghanbari, Mojtaba; Nuraei, Rahele

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0145-1

AbstractRecently, Ghanbari and Mahdavi-Amiri focused on solving LR fuzzy linear systems by use of ranking functions. They applied a ranking function introduced by Cheng, which is based on the centroid point, to illustrate their method. Also, they presented an important lemma using the centroid formulae provided by Cheng, to determine the centroid point for a class of fuzzy numbers. Unfortunately, they didn't consider that the formulae are incorrect and have led to some misapplications as pointed out by Wang and his colleagues. Therefore, in this paper, we first show that Lemma 19 of Ghanbari and Mahdavi-Amiri's paper is not true and then correct it using the centroid formulae suggested by Wang. Finally, we correct the results obtained in Ghanbari and Mahdavi-Amiri's paper for a special example.

Fattahi, Fatemeh

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0152-2

AbstractAt the present paper, the new concepts of fuzzy quasi norm, fuzzy Banach space, fuzzy quasi continuity and fuzzy quasi boundedness is introduced. Furthermore, we define the fuzzy quasi operator norm and also it is shown that the set all of fuzzy quasi bounded operator from X to Y is fuzzy quasi Banach space. Finally, we have introduced and investigated some notions and some results on *-algebra theory.

Sakawa, M.; Matsui, T.; Katagiri, H.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0146-0

AbstractThis paper considers multiobjective integer programming problems where each coefficient of the objective functions is expressed by a random fuzzy variable. A new decision making model is proposed by incorporating the concept of probability maximization into a possibilistic programming model. For solving transformed deterministic problems, genetic algorithms with double strings for nonlinear integer programming problems are introduced. An interactive fuzzy satisficing method is presented for deriving a satisficing solution to a decision maker by updating the reference probability levels. An illustrative numerical example is provided to clarify the proposed method.

George, Raji; Johnson, T. P.

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0148-y

AbstractWe investigate the lattice structure of the set of all stratified principal L-topologies on a given set X. It proves that the lattice of stratified principal L-topologies S p(X) has atoms and dual atoms if and only if L has atoms and dual atoms respectively. Moreover, it is complete and semi-complemented. We also discuss some other properties of the lattice.

Chutia, Rituparna

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0150-4

AbstractIn almost all the realistic circumstances, such as health risk assessment and uncertainty analysis of atmospheric dispersion, it is very essential to include all the information into modelling. The parameters associated to a particular model may include different kind of variability, imprecision and uncertainty. More often, it is seen that available informations are interpreted in probabilistic sense. Probability theory is a well-established theory to measure such kind of variability. However, not all of available information, data or model parameters affected by variability, imprecision and uncertainty can be handled by traditional probability theory. Uncertainty or imprecision may occur due to incomplete information or data, measurement errors or data obtained from expert judgement or subjective interpretation of available data or information. Thus, model parameters, data may be affected by subjective uncertainty. Traditional probability theory is inappropriate to represent them. Possibility theory and fuzzy set theory is another branch of mathematics which is used as a tool to describe the parameters with insufficient or vague knowledge. In this paper, an attempt has been made to combine probability knowledge and possibility knowledge and draw the uncertainty. The paper describes an algorithm for combining probability distribution and interval-valued fuzzy number and applied to environmental risk modelling with a case study. The primary aim of this paper is to propagate the proposed method. Computer codes are prepared for the proposed method using MATLAB.

Kheiri, Zeinab; Zahmatkesh, Faezeh; Cao, Bing-Yuan

2013 Fuzzy Information and Engineering

doi: 10.1007/s12543-013-0147-z

AbstractThis paper presents a method for solving posynomial geometric programming with fuzzy coefficients. By utilizing comparison of fuzzy numbers with a method, the programming with fuzzy coefficients is reduced to the programming with constant coefficients. Then the programming with fuzzy coefficients can be solved by using a method for posynomial geometric programming. Finally, one comparative example is used to illustrate advantage of the new method.