There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural ne...

Risk-based management of environmental systems, like rivers, lakes, groundwater aquifers and coastal areas, is a very useful approach for combating specific problems, like water pollution and loss of ecosystems biodiversity. Uncertainties that could be inherent to natural variabilities in space and time, such as those due to hydrological and climatic variations, together with uncertainties rela...

This paper adopts the spread of fuzzy variable as a new criteria in practical risk management problems, and develops a novel fuzzy expectation-spread (E-S) model for portfolio optimization problem. Since the spread is defined by Lebesgue-Stieltjes (L-S) integral, its computation for general fuzzy variables is a challenge issue for research, and usually depends on approximation scheme and soft c...

A description of quantitative dependencies by a novel type of fuzzy rules like ‘If X is SMALL then Y is QUICKLY INCREASING’ is considered. The use of such rules for representation of perception based and numerical information about dependencies between variables is discussed. These rules are based on a granulation of directions of function change or slope values. Perception based information gi...

In this paper, we focus on multiobjective fuzzy random programming problems with simple recourse through a fractile optimization model, in which fuzzy random variables coefficients are involved in equality constraints, and random variables coefficients are involved in the objective functions. In the proposed method, equality constraints with fuzzy random variables are defined on the basis of a ...

The problem of deriving membership functions as a means for describing linguistic variables (for some control process) and the choice of fuzzy inference operators and connectives is at the heart of developing fuzzy control systems. Over the years con-nectionist systems have obtained prominence as a means to solve complicated learning tasks. More recently a surge in interest for applying neural ...

This paper addresses the problem of stabilizing a class of nonlinear systems by using an fuzzy output feedback controller. First, a class of nonlinear systems is approximated by a Takagi–Sugeno (TS) fuzzy model. Then, based on a well-known Lyapunov functional approach, we develop a technique for designing an fuzzy output feedback control law which guarantees the 2 gain from an exogenous input t...

The authors [2-5] have studied and presented the quantitative method of linguistic variables and linguistic threshold by fuzzy set. Chien-Hua Wang, Chin-Pang Tzong proposed an algorithms for mining fuzzy association rule [2]. In this paper, we extend the algorithms proposed in [2] for number data and linguistic variables by using hedge algebras.

This paper addresses a type of fully fuzzy bilevel linear programming (FFBLP) wherein all the coefficients and decision variables in both the objective function and constraints are triangular fuzzy numbers. This paper proposes a new simple-structured, efficient method for FFBLP problems based on crisp bilevel programming that yields fuzzy optimal solutions with unconstraint variables and parame...