• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.2
• /
• pp.197-202
• /
• 2015

A triangular fuzzy number is one of the most popular fuzzy numbers. Many results for the extended algebraic operations between two triangular fuzzy numbers are well-known. We generalize the triangular fuzzy numbers on $\mathbb{R}$ to $\mathbb{R}^2$. By defining parametric operations between two regions valued ${\alpha}$-cuts, we get the parametric operations for two triangular fuzzy numbers defined on $\mathbb{R}^2$.

• Communications for Statistical Applications and Methods
• /
• v.15 no.4
• /
• pp.517-530
• /
• 2008

In this paper we introduce a time series model using the triangle fuzzy numbers in order to construct a statistical relation for the data which is a sequence of observations which are ordered in time. To estimate the proposed fuzzy model we split of a universal set includes all observation into closed intervals and determine a number and length of the closed interval by the frequency of events belong to the interval. Also we forecast the data by using a difference between observations when the fuzzified numbers equal at successive times. To investigate the efficiency of the proposed model we compare the ordinal and the fuzzy time series model using examples.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.4
• /
• pp.398-402
• /
• 2014

A generalized trigonometric fuzzy set is a generalization of a trigonometric fuzzy number. Zadeh([7]) defines the probability of the fuzzy event using the probability. We define the normal and exponential fuzzy probability on $\mathbb{R}$ using the normal and exponential distribution, respectively, and we calculate the normal and exponential fuzzy probability for generalized trigonometric fuzzy sets.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.2
• /
• pp.212-217
• /
• 2012

A fuzzy set $A$ defined on a probability space ${\Omega}$, $\mathfrak{F}$, $P$ is called a fuzzy event. Zadeh defines the probability of the fuzzy event $A$ using the probability $P$. We define the generalized triangular fuzzy set and apply the extended algebraic operations to these fuzzy sets. A generalized triangular fuzzy set is symmetric and may not have value 1. For two generalized triangular fuzzy sets $A$ and $B$, $A(+)B$ and $A(-)B$ become generalized trapezoidal fuzzy sets, but $A({\cdot})B$ and $A(/)B$ need not to be a generalized triangular fuzzy set or a generalized trapezoidal fuzzy set. We define the normal fuzzy probability on $\mathbb{R}$ using the normal distribution. And we calculate the normal fuzzy probability for generalized triangular fuzzy sets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.387-390
• /
• 2009

패턴 분류 문제는 기계 학습 분야에서 매우 중요한 연구 주제이다. 하지만 불완전 데이터는 실생활에서 매우 빈번히 발생 할 뿐만 아니라 분류 모델의 학습도가 낮다는 문제점을 지니고 있다. 불완전한 데이터를 다루는 것에 대한 많은 방법들이 제안되어 왔지만 대부분의 방법들이 훈련 단계에 집중하고 있다. 본 논문에서는 삼각 형태의 퍼지 함수를 이용하여 불완전 데이터의 분류 알고리즘을 제안한다. 제안한 기법에서는 불완전한 특징 벡터에서의 불완전 데이터를 추론하고 학습하였으며, 추론된 데이터의 가중치를 삼각 퍼지 함수 분류기에 적용하였다. 실험을 통하여 제안한 기법이 상대적으로 높은 인식률을 나타냄을 확인할 수 있었다.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.36C no.11
• /
• pp.84-91
• /
• 1999

In this paper, we could analyze the comparison with applied WFM to the quantization triangularity fuzzy function and triangularity Fuzzy function. In order to improve on a fault which not remove completely noise included image according to a peculiarity of noise, we got to realize FDNN of the high speed weight eliminating noise included image, minimizing the lost of information, obtaining information of suitability owing to applied Fuzzy Algorithm to DBNN of a hierarchical structure. We could analyze the comparison with a power of WFM and FDNN using simulation We could find to superiority the proposed FDNN )n a result which was the comparison of MSE for the boats image.

• Journal of Digital Convergence
• /
• v.13 no.4
• /
• pp.227-233
• /
• 2015

The recent health care is growing rapidly want to receive offers users a variety of medical services, can be exploited easily exposed to a third party information on the role of the patient's hospital staff (doctors, nurses, pharmacists, etc.) depending on the patient clearly may have to be classified. In this paper, in order to ensure safe use by third parties in the health care environment, classify the attributes of patient information and patient privacy protection technique using hierarchical multi-property rights proposed to classify information according to the role of patient hospital officials The. Hospital patients and to prevent the proposed method is represented by a mathematical model, the information (the data consumer, time, sensor, an object, duty, and the delegation circumstances, and so on) the privacy attribute of a patient from being exploited illegally patient information from a third party the prevention of the leakage of the privacy information of the patient in synchronization with the attribute information between the parties.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.04a
• /
• pp.496-500
• /
• 1995

전문가 내지 구성원의 주관적인 의견에 의존하는 방법의 하나인 델파이법(Delphi Method)은 관련자료가 불충분한 중.장기 예측, 전략결정 등에 이용되고 있다. 이 방법을 더욱 발전시킨 퍼지 델파이법(Fuzzy Delphi Method)은 델파이법에 퍼지숫자(fuzzy number)의 개념을 도입하여 정확한 예측을 하고자 하는 것이다. 또한 이러한 예측치가 삼각 퍼지 숫자(Triangular Fuzzy Number)로 주어져 불확실성에 대한 예측과 의견종합을 쉽게 하며, 전문가에 의해 추정된 삼각 퍼지 숫자의 입력을 토애 그 추정치들의 비유사도(Dissemblance Index)와 퍼지거리(fuzzy distance)를 계산하고 간단한 그래프를 다시 전문가에게 피드백(feedback)할 수 있도록 나타내어지는 과정을 code화하여 전문가들로 하여금 다양한 정보를 통하여 좀 더 정확한 추정치를 예측하고자 한다.

• Journal of the Korean Data and Information Science Society
• /
• v.5 no.1
• /
• pp.49-57
• /
• 1994

기존의 퍼지회귀모형은 모수의 퍼지성질에 의해 관측된 종속변수의 변동을 설명하는 방법이다. 그러나 일반적으로 종속변수에 영향을 미치는 모든 독립변수를 모형화하는 일은 불가능하므로 종속변수가 삼각퍼지숫자로 관측된 경우 모형화되지 않은 변수들의 영향을 랜덤 오차항으로 두는 퍼지확률회귀모형을 소개하고 이에 따른 모수추정법을 다룬다. 이 방법은 통계적 회귀모형의 일반화로 간주할 수 있다.

• Journal of IKEEE
• /
• v.18 no.1
• /
• pp.57-63
• /
• 2014

This paper is to technically implement the sensing platform for Home-Wellness Robot. The self-Localization of indoor mobile robot is very important for the sophisticated trajectory control. In this paper, the robot's self-localization algorithm is designed by RF sensor network and fuzzy inference. The robot realizes its self-localization, using RFID sensors, through the collaboration algorithm which uses fuzzy inference for combining the strengths of triangulation and triangulation. For the triangulation self-Localization, RSSI is implemented. TOA method is used for realizing the triangulation self-localization. The final improved position is, through fuzzy inference, made by the fusion algorithm of the resultant coordinates from trilateration and triangulation in real time. In this paper, good performance of the proposed self-localization algorithm is confirmed through the results of a variety of experiments in the base of RFID sensor network and reader system.