• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.8
• /
• pp.715-719
• /
• 2001

In this paper, we study the existence and uniqueness of fuzzy solution for the nonlinear fuzzy differential equations with nonlocal initial condition in E$^{2}$$_{N}$

• Journal of Korean Institute of Industrial Engineers
• /
• v.23 no.2
• /
• pp.307-321
• /
• 1997

In this paper, we consider the expert's ambiguity and the decision maker's fuzzy goals which are incorporated into multiobjective nonlinear programming problems in order to find a compensatory solution. The proposed method can be applied to all cases of multiobjective problems with fuzzy parameters since the interactive process with a decision maker is simple, various uncertainties involved in decision making are eliminated and all the objectives are well balanced. An illustrative numerical example for nonlinear programming problems with fuzzy parameters is demonstrated along with the corresponding computer output.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.357-360
• /
• 2001

In this paper, we study the existence and uniqueness of fuzzy solution for the nonlinear fuzzy differential equations with nonlocal initial condition in E$\sub$N/$\^$2/ by using the concept of fuzzy number of dimension 2 whose values are normal convex upper semicontinuous and compactly supported surface in R$_2$.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.41
• /
• pp.41-50
• /
• 1997

In this paper, we presents the method for finding the compensatory solution for fuzzy multiobjective nonlinear programming problem with fuzzy parameters involved in the problem-formulation process and fuzzy equal goals of the decision maker for each of the objective functions. The fuzzy parameters in the objective functions and the constraints characterized by fuzzy numbers. The proposed method can be applied to case with multiobjective problems and guarantee an efficient solution. An illustrative numerical example is presented.

• Kyungpook Mathematical Journal
• /
• v.53 no.1
• /
• pp.87-98
• /
• 2013

In this paper, we devoted to study the existence and uniqueness of nonlinear fuzzy neutral integrodifferential equations. Moreover we study the fuzzy solution for the normal, convex, upper semicontinuous, and compactly supported interval fuzzy number. The results are obtained by using the Banach fixed-point theorem. An example is provided to illustrate the theory.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.1-4
• /
• 2003

This paper we study the existence of a global solution for the nonlinear fuzzy differential equations in E$_{N}$$^{n}$ by using the concept of fuzzy number of dimension n whose values are normal, convex, upper semicontinuous and compactly supported surface in R$^{n}$ . .

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.2
• /
• pp.68-73
• /
• 2000

This paper addresses the analysis and design of fuzzy control systems for a class of complex nonlinear systems. Firstly, the nonlinear system is represented by Takagi-Sugeno(TS) fuzzy model and the global controller is constructed by compensating each linear model in the rule of TS fuzzy model. The design of conventional TS fuzzy-model-based controller is composed of two processes. One is to determine the static state feedback gain of each local model and the other is to validate the stability of the designed fuzzy controller. In this paper, we propose an alternative methods for the design of TS fuzzy-model-based controller. The design scheme is based on the extension of conventional optimal control theory to the design of TS fuzzy-model-based controller. By using the proposed method, the design and stability analysis of the TS fuzzy model-based controller is reduced to the problem of finding the solution of a set of algebraic Riccati equations. And we use the recently developed interior point method to find the solution of AREs, where AREs are recast as the LMI formulation. A numerical simulation example is given to show the effectiveness and feasibiltiy of the proposed fuzzy controller design method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.19 no.39
• /
• pp.75-80
• /
• 1996

Fuzzy approaches used to solve MONLP(Multiobjective Nonlinear Programming Problem) are based on the max-min method of fuzzy sets theory However, since the min operator noncompensatory, these approaches can not guarentee an efficient solution to the problem. In this paper, we presents an algorithm for finding the aggregation operator to find efficient solution. In particular, our presented algorithm is guarentee an efficient solution. On the basis of proposed algorithm, an illustrative numerical example is presented.

• East Asian mathematical journal
• /
• v.34 no.1
• /
• pp.47-58
• /
• 2018

In this paper, we consider a fuzzy nonlinear random variational inclusion problems involving ordered RME-multivalued mapping in ordered Banach spaces. By using the random relaxed resolvent operator and its properties, we suggest an random iterative algorithm. Finally both the existence of the random solution of the original problem and the convergence of the random iterative sequences generated by random algorithm are proved.

• Communications for Statistical Applications and Methods
• /
• v.17 no.2
• /
• pp.141-151
• /
• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.