This paper studies fuzzy filtering design for state estimation of nonlinear discrete-time systems with bounded but unknown disturbances. First, the Takagi and Sugeno fuzzy model is proposed to represent a nonlinear system. Next, using a linear matrix inequality (LMI) approach, the fuzzy filtering problems are characterized in terms of a linear matrix inequality problem (LMIP). The LMIP can be e...

Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multiclass problem. In this paper we consider the optimi...

This paper shows a general model of a Mixed Production Planning problem with fuzzy demands. The main focus is the development of a model for Production Planning using fuzzy sets in order to use classical mathematical programming techniques to reach an optimal solution over a multiple criteria context. The classical Fuzzy Linear Programming model namely the Soft Constraints Model is used to invo...

This paper presents a number of state feedback controller design methods for fuzzy dynamic systems with H optimization and/or additional constraints on the closed-loop pole location. The controller design involves solving a set of linear matrix inequalities and the control laws are numerically tractable via LMI techniques. The global stability of the closedloop fuzzy control system is also esta...

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning (ERP). It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem...

We study the information processing properties of a binary channel receiving data from a gaussian source. A systematic comparison with linear processing is done. A remarkable property of the binary sytem is that, as the ratio between the number of output and input units increases, binary processing becomes equivalent to linear processing with a quantization output noise that depends on. In this...

The method for obtaining the fuzzy estimates of regression parameters with the help of “Resolution Identity” in fuzzy sets theory is proposed. The -level least-squares estimates can be obtained from the usual linear regression model by using the -level real-valued data of the corresponding fuzzy input and output data. The membership functions of fuzzy estimates of regression parameters will be ...

This paper introduces a new fuzzy control technique, which belongs to the popular family of control algorithms, called Model Predictive Controllers. The method is based on a dynamic fuzzy model of the process to be controlled, which is used for predicting the future behavior of the output variables. A non-linear optimization problem is then formulated, which minimizes the di3erence between the ...

The modeling and solving the optimization problem is one of the most important daily problem.By notation the nature of data in practice which are imprecise, fully fuzzy linear programming problem (FFLP) is a power full tool to modeling the practical optimization problem. In This paper after introducing FFLP, a new method to solve it is proposed. a linear ranking function for defuzzifying the FF...