• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.130-133
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• 2000

In this paper, a new design methodology named FNNN(Fuzzy Polynomial Neural Network) algorithm is proposed to identify the structure and parameters of fuzzy model using PNN(Polynomial Neural Network) structure and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and modified quadratic besides the biquadratic polynomial used in the GMDH. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture Several numerical example are used to evaluate the performance of out proposed model. Also we used the training data and testing data set to obtain a balance between the approximation and generalization of proposed model.

• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.49-57
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• 2021

In the case of military supplies, any potential failure and causes of failures must be considered. This study is aimed at examining the failure modes of a rotorcraft landing system to identify the priority items. Failure mode and effects analysis (FMEA) is applied to the rotorcraft landing system. In general, the FMEA is used to evaluate the reliability in engineering fields. Three elements, specifically, the severity, occurrence, and detectability are used to evaluate the failure modes. The risk priority number (RPN) can be obtained by multiplying the scores or the risk levels pertaining to severity, occurrence, and detectability. In this study, different weights of the three elements are considered for the RPN assessment to implement the FMEA. Furthermore, the FMEA is implemented using a fuzzy rule base, similarity aggregation model (SAM), and grey theory model (GTM) to perform a comparative analysis. The same input data are used for all models to enable a fair comparison. The FMEA is applied to military supplies by considering methodological issues. In general, the fuzzy theory is based on a hypothesis regarding the likelihood of the conversion of the crisp value to the fuzzy input. Fuzzy FMEA is the basic method to obtain the fuzzy RPN. The three elements of the FMEA are used as five linguistic terms. The membership functions as triangular fuzzy sets are the simplest models defined by the three elements. In addition, a fuzzy set is described using a membership function mapping the elements to the intervals 0 and 1. The fuzzy rule base is designed to identify the failure modes according to the expert knowledge. The IF-THEN criterion of the fuzzy rule base is formulated to convert a fuzzy input into a fuzzy output. The total number of rules is 125 in the fuzzy rule base. The SAM expresses the judgment corresponding to the individual experiences of the experts performing FMEA as weights. Implementing the SAM is of significance when operating fuzzy sets regarding the expert opinion and can confirm the concurrence of expert opinion. The GTM can perform defuzzification to obtain a crisp value from a fuzzy membership function and determine the priorities by considering the degree of relation and the form of a matrix and weights for the severity, occurrence, and detectability. The proposed models prioritize the failure modes of the rotorcraft landing system. The conventional FMEA and fuzzy rule base can set the same priorities. SAM and GTM can set different priorities with objectivity through weight setting.

• International Journal of Reliability and Applications
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• v.12 no.2
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• pp.79-94
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• 2011

Redundancy-reliability allocation problems in multi-stage series-parallel systems are addressed in this study. Fuzzy programming techniques are proposed for finding satisfactory solutions. First, a multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get satisfactory solutions for the fuzzy nonlinear programming problem. A Pareto optimal solution is found with maximal degree of satisfaction from the interception area of fuzzy sets. A case study that is related to the electronic control unit installed on aircraft engine over-speed protection system is used to implement the developed approach. Results suggest that the developed fuzzy multi-objective programming model can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase.

• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.101-106
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• 1992

In this paper fuzzy goal programming problems with fuzzy constraints and fuzzy coefficients in both matrix and right hand side of the constraints set are considered. Because of fuzzy coefficients in both members of each constraint ranking methods for fuzzy numbers are considered. An additive model to solve fuzzy goal programming problems is formulated. The diversity of each methods provides a lot of different models of auxiliary linear programming problems from which fuzzy solutions to the fuzzy goal programming problem can be obtained.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.591-601
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• 2005

The results of fuzzy linear regression are very sensitive to irregular data. When this points exist in a set of data, a fuzzy linear regression model can be incorrectly interpreted. The purpose of this paper is to detect irregular data and to propose robust fuzzy linear regression based on M-estimators with triangular fuzzy regression coefficients for crisp input-output data. Numerical example shows that irregular data can be detected by using the residuals based on M-estimators, and the proposed robust fuzzy linear regression is very resistant to this points.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.528-530
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers. this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. Furthermore, stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. A simulation is included for the control of the Duffing forced-oscillation system, to show the effectiveness and feasibility of the proposed fuzzy control method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.185-189
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers, this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. The stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. Furthermore, the proposed method can be applied to partially known uncertain nonlinear systems. A numerical simulation is performed for the control of an inverted pendulum, to show the effectiveness and feasibility of the proposed fuzzy control method.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.80-87
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• 1998

This paper presents and online scheme for fuzzy modelling of nonlinear systems, based on the model adjustment technique and the genetic algorithm technique. The fuzzy model is characterized by fuzzy "if-then" rules which represent locally linear input-output relations whose consequence parts are defined as subsystems of a nonlinear sysem. The discrete-time model for each subsystem is obtained to deal with initalization and unmeasurable signal problems in online estimation and the final output of the fuzzy model is computed from the outputs of the discrete-time models. Then, the parameters of both the premise and consequence parts of the fuzzy model are adjusted by a genetic algorithm. A set of simulation works is carried out to demonstrate the effectiveness of the proposed method.ed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.198-203
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• 2002

In this paper, a 3D model of the surface deformation is created in virtual environment. A proposed method is based on the fuzzy model and it is enough that only one rule set is added to the fuzzy model to model a surface deformation. Furthermore, the designer can easily determine which parameters should be used and how they should be changed in order to obtain the shapes as required. The proposed method is, thus, a simple, but effective technique that can also be used in practical applications. The results of the computer simulation are also given to demonstrate the validity of the proposed algorithm.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.173-181
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• 2024

The healthy products dedicated for young people are qualified as a solution to protect the future generation, especially that most commercial deals do not consider the consumer's health and environment. Therefore, it is crucial to define the antecedent of healthy purchases and to examine their impact on teenagers. This research aims to explore the antecedents and the consequences of the consumption of Saudis teenagers. Therefore, we develop a research model in the conceptual framework and the hypotheses to test. The empirical analysis required two samples from Saudis youth consumers. The first sample was utilized in the exploratory study with SPSS software. Then, the second was employed to the confirmatory part with the Amos software, as well as the validation of the hypotheses, and model with Fuzzy Set approach. The findings of this study have significant insights into the Saudi consumption and implications for both practitioners and researchers. Then, we have particularly strenuous on intention purchase antecedents of organic foods, and their consume habit moderation.