In this paper, a design method for a robust PI/PID controller for fuzzy parametric uncertain systems is proposed. An uncertain Linear Time Invariant (LTI) system with fuzzy coefficients is referred as fuzzy parametric uncertain system. The fuzzy coefficients are approximated by intervals (crisp sets) using the nearest interval approximation approach to obtain an interval system. Further, a robu...

In this paper, we are dealing with a novel data-driven learning method (SparseFIS) for Takagi-Sugeno fuzzy systems, extended by including rule weights. Our learning method consists of three phases: the first phase conducts a clustering process in the input/output feature space with iterative vector quantization. Hereby, the number of clusters = rules is pre-defined and denotes a kind of upper b...

in a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. the use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. the simple and basic idea in this paper i...

Many problems can be presented in an abstract form through a wide range of binary objects and relations which are defined over problem’s domain. In these problems, graphical demonstration of defined binary objects and solutions is the most suitable representation approach. In this regard, graph drawing problem discusses the methods for transforming combinatorial graphs to geometrical drawings i...

in this paper, a new method based on parametric form for approximate solu-tion of fuzzy linear matrix equations (flmes) of the form ax = b; where ais a crisp matrix, b is a fuzzy number matrix and the unknown matrix x one,is presented. then a numerical example is presented to illustrate the proposedmodel.

this paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. this estimation method is obtained by implementing ridge regression learning algorithm in the la- grangian dual space. the distance measure for fuzzy numbers that suggested by diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

a‎s we know, developing mathematical models and numerical procedures that would appropriately treat and solve systems of linear equations where some of the system's parameters are proposed as fuzzy numbers is very important in fuzzy set theory. for this reason, many researchers have used various numerical methods to solve fuzzy linear systems. in this paper, we define the concepts of midpoint a...

Linear programming problems with fuzzy coefficients in the objective function are considered. Emphasis is on the dependence of the optimal solution from linear perturbations of the membership functions of the objective function coefficients as well as on the computation of a robust solution of the fuzzy linear problem if the membership functions are not surely known.

One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two conflicting objectives (i.e., accuracy maximization and complexity minimization) when we design accurate and compact fuzzy rule-based systems from numerical data. Since the main advantage of fuzzy rule-based systems co...

In this paper, we introduce a numerical method to determine the stability set of the first kind for fuzzy multi-objective non-linear programming (FMONP) problem. An α−level set of the fuzzy vector is used together with a weighting approach to transform (FMONP) problem to a single-objective inequality constrained optimization (SOICO) problem. An active set strategy is used together with a multip...