• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.37-40
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• 1997

This paper presents a performance improvement of distributed fuzzy control system by changing the triangular membership function widths according to the input variables. The control region consists of 4 parts according to the sign of error and change of error terms. Each control part is operated by the suitable nonuniform triangular membership function. Through the simulation for the boiler-turbin model of a fossil power plant using decentralized contol, it is verified this proposal

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.201-208
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• 2017

This paper propose triangular fuzzy membership function to make model that based on awareness of human in the love model that with external force, which have the basic love model of Romeo and Juliet. This paper represents the phenomena of behaviors by time series and phase portraits after using this fuzzy triangular membership function as an external force and also confirms existence of nonlinear characteristics.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.87-103
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• 2018

This paper introduces the method to develop two main types of the fuzzy OEE (Overall Equipment Effectiveness) models via triangular membership function for measuring uncertainty. The fuzzy OEE includes model type 1 and model type 2. The model type 1 is used when the theoretical machine speed only reflects the time loss whereas model type 2 is used when the actual machine speed reflects both time and speed loss. Model type 2 has shown to perform a lower availability rate and a higher performance rate compared to model type 1. In addition, the fuzzy UPH (Unit Per Hour) which is derived from using the fuzzy OEE is presented to satisfy demand uncertainty. The fuzzy UPH can easily measure the fuzzy tact time and cycle time by reciprocating itself. Finally, this study demonstrates the fuzzy OEE models using IVIFS (Interval-Valued Intuitionistic Fuzzy Set) based on the characterization via membership function, non-membership function and hesitant function. For the purpose of analyzing the fuzzy system OEE, the OEE for each machine of plant structure is considered triangular interval-valued intuitionistic fuzzy number. Regardless of plant structure, the validity degree of fuzzy membership function of system OEE decreases when the number of machine with worst value of the validity degree increases. Corresponding examples are presented in this paper for practitioner to understand the applicability and practicability of the proposed fuzzy OEE methods.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.383-386
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• 2010

Pattern classification is one of the most important topics for machine learning research fields. However incomplete data appear frequently in real world problems and also show low learning rate in classification models. There have been many researches for handling such incomplete data, but most of the researches are focusing on training stages. In this paper, we proposed two classification methods for incomplete data using triangular shaped fuzzy membership functions. In the proposed methods, missing data in incomplete feature vectors are inferred, learned and applied to the proposed classifier using triangular shaped fuzzy membership functions. In the experiment, we verified that the proposed methods show higher classification rate than a conventional method.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.217-224
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• 2017

Recently, we have been continued effort that chaotic theory apply into love model which is an area of social science. To make the chaotic behaviors in the differential equation that represent as Romeo and Juliet, we apply an external force to the differential equation. However, this external force have disadvantage that cannot exactly represent for emotion of human. In this paper, to solve these advantage, we introduce triangular fuzzy membership function to provide the external force that can describe most similar status for action and word of human in the love model of Romeo and Juliet. Also, to confirm the chaotic behaviors in the love model of Romeo and Juliet with proposed fuzzy membership function, we use time series and phase plane.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.445-449
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• 2005

Yen et al. [Fuzzy Sets and Systems 106 (1999) 167-177] calculated the fuzzy membership function for the output to find the non-symmetric triangular fuzzy number coefficients of a linear regression model for all given input-output data sets. In this note, we show that the result they obtained in their paper is invalid.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.1
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• pp.36-43
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• 1986

In this study, the fuzzy programming is extended to handle various types of membership functions by transformation of the complicated fuzzy programming problems into the equivalent crisp linear programming problems with single objective. It is well-known that the fuzzy programming problem with linear membership functions (i.e., ramp type) can be easily transformed into a linear programming problem by introducing one dummy variable to minimize the worst unwanted deviation. However, until recently not many researches have been done to handle various general types of complicated linear membership functions which might be more realistic than ramp-or triangular-type functions. In order to handle these complicated membership functions, the goal dividing concept, which is based on the fuzzy set operation (i. e., intersection and union operations), has been prepared. The linear model obtained using the goal dividing concept is more efficient and single than the previous models [4, 8]. In addition, this result can be easily applied to any nonlinear membership functions by piecewise approximation since the membership function is continuous and monotone increasing or decreasing.

• Environmental Engineering Research
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• v.23 no.1
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• pp.107-113
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• 2018

Air quality and pollution have recently become a major concern; vehicle emissions significantly pollute the air, especially in large and crowded cities. There are various factors that affect vehicle emissions; this research aims to find the most influential factors affecting $CO_2$ and $NO_x$ emissions using Adaptive Neural Fuzzy Inference System (ANFIS) as well as a systematic approach. The modified ANFIS (MANFIS) was developed to enhance modelling and Root Mean Square Error was used to evaluate the model performance. The results show that percentages of $CO_2$ from trucks represent the best input combination to model. While for $NO_x$ modelling, the best pair combination is the vehicle delay and percentage of heavy trucks. However, the final MANFIS structure involves two inputs, three membership functions and nine rules. For $CO_2$ modelling the triangular membership function is the best, while for $NO_x$ the membership function is two-sided Gaussian.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.298-301
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• 1996

This paper describes the structure Identification of nonlinear function using Adaptive Neuro-Fuzzy Inference Technique(ANFIS). Nonlinear mapping relationship between inputs and outputs were modeled by Sugeno-Takaki's Fuzzy Inference Method. Specially, the consequent parts were identified using a series of 1st order equations and the antecedent parts using triangular type membership function or bell type ones. According to learning Rules of ANFIS, adjustable parameters were converged rapidly and accurately.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.