• The Pure and Applied Mathematics
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• v.24 no.1
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• pp.45-51
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• 2017

In this paper, we propose an accelerating scheme of convergence of numerical solutions of fuzzy non-linear equations. Numerical experiments show that the new method has significant acceleration of convergence of solutions of fuzzy non-linear equation. Three-dimensional graphical representation of fuzzy solutions is also provided as a reference of visual convergence of the solution sequence.

• The Pure and Applied Mathematics
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• v.27 no.1
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• pp.35-42
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• 2020

In this paper, numerical algorithms for solving a fuzzy system of linear equations with crisp coefficients are presented. We illustrate the efficiency and accuracy of the proposed methods by solving some numerical examples. We also provide a graphical representation of the fuzzy solutions in three-dimension as a visual reference of the solution of the fuzzy system.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.831-846
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• 2011

There are several methods, in the literature, for finding the fuzzy optimal solution of fully fuzzy transportation problems (transportation problems in which all the parameters are represented by fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings, a new method (based on fuzzy linear programming formulation) is proposed to find the fuzzy optimal solution of unbalanced fuzzy transportation problems with a new representation of trapezoidal fuzzy numbers. The advantages of the proposed method over existing method are discussed. Also, it is shown that it is better to use the proposed representation of trapezoidal fuzzy numbers instead of existing representation of trapezoidal fuzzy numbers for finding the fuzzy optimal solution of fuzzy transportation problems. To illustrate the proposed method a fuzzy transportation problem (FTP) is solved by using the proposed method and the obtained results are discussed. The proposed method is easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems occurring in real life situations.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.333-341
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• 2004

In the real world there are many linear programming problems where all decision parameters are fuzzy numbers. Several approaches exist which use different ranking functions for solving these problems. Unfortunately when there exist alternative optimal solutions, usually with different fuzzy value of the objective function for these solutions, these methods can not specify a clear approach for choosing a solution. In this paper we propose a method to remove the above shortcoming in solving fuzzy number linear programming problems using the concept of expectation and variance as ranking functions

• The Pure and Applied Mathematics
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• v.24 no.3
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• pp.147-153
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• 2017

In this paper, we propose a numerical method to solve fuzzy differential equations. Numerical experiments show that when the step size is small, the new method has significantly good approximate solutions of fuzzy differential equation. Graphical representation of fuzzy solutions in three-dimension is also provided as a reference of visual convergence of the solution sequence.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.70-78
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• 1997

Proportional-integral-derivative(P1D) controllers have been still widely used in industrial processes due to their simplicity, effectiveness, robustness for a wide range of operating conditions, and the familiarity of control engineers. And a number of recent papers in fuzzy systems are showing that fuzzy systems are universal approximators. That is, fuzzy controllers are capable of approximating any real continuous function on a compact set of arbitrary accuracy. In this paper, we derive the linear PID control law from the fuzzy control algorithm where all fuzzy sets for representing plant state variables and a control variable use common triangular types. We first lead a linear PD control law from a fuzzy logic control with only two fuzzy sets for error and change-of-error. And then we derive the linear PID control law from a fuzzy controller. We here assumed that the intervals of error, change-of-error, and integral error could be partitioned into arbitrary numbers, respectively. As a result, a linear PID controller is only a sort of various fuzzy logic controls.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.941-947
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• 2002

Two methods of constructing T-S fuzzy model which is equivalent to a given nonlinear system are presented. The first method is to obtain an equivalent T-S fuzzy model by using the sum of linearly independent scalar functions with constant real matrix coefficients. The sum of products of linearly independent scalar functions is used in the second method. The former method is to formulate the procedures of T-S fuzzy modeling dealt in many examples of previous publications; the latter is a new method. By comparing the number of linearly independent functions used in the two methods, we can easily find out which method makes fewer rules than the other. The nonlinear dynamics of an inverted Pendulum on a cart is used as an equivalent T-5 fuzzy modeling example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.189-197
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• 1999

Assembly line balancing problem is known as one of difficult combinatorial optimization problems. This problem has been solved with linear programming, dynamic programming approaches. but unfortunately these approaches do not lead to efficient algorithms. Recently, genetic algorithm has been recognized as an efficient procedure for solving hard combinatorial optimization problems, but has a defect that requires long-run time and computational complexties to find the solution. For this reason, we adapt a new method called the Critical Node Method that is intuitive, easy to understand, simple for implementation. Fuzzy set theory is frequently used to represent uncertainty of information. In this paper, to treat the data of real world problems we use a fuzzy number to represent the duration and Critical Node Method based heuristic procedure is developed for solving fuzzy assembly line balancing problem.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.296-300
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• 2003

The autopilot system targets decreasing labor, working environment improvement, service safety security and elevation of service efficiency. Ultimate purpose is minimizing number of crew for guarantee economical efficiency of shipping service. Recently, being achieving research about Course Keeping Control, Track Keeping Control, Roll-Rudder Stabilization. Dynamic Ship Positioning and Automatic Mooring Control etc. which compensate nonlinear characteristic using optimizing control technique. And application research is progressing using real ship on actual field. Relation of Rudder angle which adjusted by Steering Machine and ship-heading angle are non-linear. And Load Condition of ship as non-linear element that influence to Parameter of ship. Also, because the speed of a current and direction of waves, velocity and quantity of wind etc. that is disturbance act in non-linear from, become factor who make serv ice of shipping painfully. Therefore, service system of shipping requires robust control algorithm that can overcome nonlinearity. In this paper, Using fuzzy algorithm ,Design autopilot system of ship that could overcome the non-linear factor of ship and disturbance and examined result through simulation.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.