• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.85-89
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• 2007

Many authors considered the computational aspect of sup-min convolution when applied to weighted average operations. They used a computational algorithm based on a-cut representation of fuzzy sets, nonlinear programming implementation of the extension principle, and interval analysis. It is well known that $T_W$(the weakest t-norm)-based addition and multiplication preserve the shape of L-R type fuzzy numbers. In this paper, we consider the computational aspect of the extension principle by the use of $T_W$ when the principle is applied to fuzzy weighted average operations. We give the exact solution for the case where variables and coefficients are L-L fuzzy numbers without programming or the aid of computer resources.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.76-78
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• 1996

When data is classified and each class has weight, the mean of data is a weighted average. When the class values and weights are trapezoidal fuzzy numbers, we can prove the weghted average is a fuzzy number though not trapezoidal. Its 4 corner points are obtained.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.117-123
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• 1996

In this paper, we present a new learning method for the fuzzy ARTMAP which is effective for the noisy input patterns. Conventional fuzzy ARTMAP employs only fuzzy AND operation between input vector and weight vector in learning both top-down and bottom-up weight vectors. This fuzzy AND operation causes excessive update of the weight vector in the noisy input environment. As a result, the number of spurious categories are increased and the recognition ratio is reduced. To solve these problems, we propose a new method in updating the weight vectors: the top-down weight vectors of the fuzzy ART system are updated using weighted average of the input vector and the weight vector itself, and the bottom-up weight vectors are updated using fuzzy AND operation between the updated top-down weitht vector and bottom-up weight vector itself. The weighted average prevents the excessive update of the weight vectors and the fuzzy AND operation renders the learning fast and stble. Simulation results show that the proposed method reduces the generation of spurious categories and increases the recognition ratio in the noisy input environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.508-514
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• 2021

In this paper, to propose a classifier based on supervised learning, three types of fuzzy membership functions that determine the membership of each feature of classification data are proposed. In addition, the possibility of improving the classifier performance was suggested by using the average value calculation method used in the process of deriving the classification result using the average value of the membership degrees for each feature, not by using a simple arithmetic average, but by using a weighted average using various weights. To experiment with the proposed methods, three standard data sets were used: Iris, Ecoli, and Yeast. As a result of the experiment, it was confirmed that evenly excellent classification performance can be obtained for data sets of different characteristics. It was confirmed that better classification performance is possible through improvement of fuzzy membership functions and the weighted average methods.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.383-386
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• 1993

In this paper, we propose a sensory data combination method by a fuzzy number approach for multisensor data fusion. Generally, the weighting of one sensory data with respect to another is derived from measures of the relative reliabilities of the two sensory modules. But the relative weight of two sensory data can be approximately determined through human experiences or insufficient experimental data without difficulty. We represent these relative weight using appropriate fuzzy numbers as well as sensory data itself. Using the relative weight, which is subjective valuation, and a fuzzy-numbered sensor data, the fuzzy weighted average method is used for a representative sensory data. The manipulation and calculation of fuzzy numbers can be carried out using the Zadeh's extension principle which can be approximately implemented by the $\alpha$-cut representation of fuzzy numbers and interval analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.3
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• pp.623-630
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• 2001

The conventional control techniques based a mathematical model are not well suited for dealing with ill-defined and uncertain system like a linear quadratic control. Recently, fuzzy control has been successfully applied to a wide variety of practical problems such as robot, water purification, automatic train operation system etc. In this paper, a design technique of robust Fuzzy-LQ controller for each subsystem is designed. Secondly , all the subsystem controllers are combined by fuzzy weighted averaging method. Finally the effectiveness of the proposed controller is verified through a series of computer simulations for an inverted pole system.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.71-74
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• 1995

In this paper, a new PID fuzzy controller(FC) based on parallel operation of PI and PD fuzzy control is presented. First, two fuzzy rule bases are constructed by separating the linguistic control rule for PID FC into two parts : one is e-.DELTA.e part, and the other is .DELTAL.$^{2}$e-.DELTA.e part. And then two FCs employing these rule bases indivisually are synthesized and run in parallel. The incremental control input is determined by taking weighted mean of the outputs of two FCs. The proposed PID FC improves the transient response of the system and gives better performance than the conventional PI FC.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1058-1062
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• 1993

A fuzzy reasoning method is proposed for the implementation of control systems based on non-fuzzy microprocessors. The essence of the proposed method is to search the local active miles instead of the global rule base. Thus the reasoning is conveniently performed on a master cell as a fuzzy accelerating kernel, which is transformed from an active fuzzy cell. The interpolative reasoning is simplified via adopting the algebraic product of fulfillment for the conditional connective AND and the weighted average for the rule sentence connective ALSO.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.315-316
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• 2011

일반적으로 영상에서 얼굴 영상을 검출하고 인식하는 알고리즘은 패턴 인식 연구에 있어서 인간과 컴퓨터의 상호작용의 연구라는 면에서 아주 중요한 문제로 연구되어 왔다. 본 논문에서는 고유얼굴을 이용하여 유클리디언 거리법과 퍼지기법의 인식률을 비교해보고자 한다. PCA(Principal Component Analysis) 방식은 우수한 인식 결과를 보장하는 얼굴인식 기법중의 하나이며, 얼굴 영상을 이용하여 공분산 행렬을 계산하고, 공분산 행렬을 통해 생성된 저차원의 벡터, 즉 고유얼굴(Eigenface)을 이용하여 가중치를 계산하고, 이 가중치를 기준으로 인식을 수행하는 기법이다. 이를 기반으로 하여, 본 논문에서는 전처리 과정, 고유얼굴 과정, 유클리디언 거리법 및 퍼지 소속도 함수 설계 과정, 신경망 학습과정, 인식과정으로 구성된 5단계의 얼굴 인식 알고리즘을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.