• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.9 no.3
• /
• pp.97-103
• /
• 1995

In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.2
• /
• pp.176-181
• /
• 2004

This paper introduces a Web site login system that uses user's native voice to improve the bother of remembering the ID and password in order to login the Web site. The DTW method that applies fuzzy inference is used as the speaker recognition algorithm. We get the ACC(Average Cepstrum Coefficient) membership function by each degree, by using the LPC that models the vocal chords, to block the recorded voice that is problem for the speaker recognition. We infer the existence of the recorded voice by setting on the basis of the number of zeros that is the value of the ACC membership function, and on the basis of the average value of the ACC membership function. We experiment the six Web sites for the six subjects and get the result that protects the recorded voice about 98% that is recorded by the digital recorder.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1626-1629
• /
• 2002

This paper describes novel methods to construct fuzzy inference rules with gradient descent. The present methods have a constructive mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create the rule unit at the nearest position from the input space, for the central value of the membership function in the antecedent part. The second is to create the rule unit which has the minimum width, for the width of the membership function in the antecedent part. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.

• Journal of Korean Institute of Industrial Engineers
• /
• v.23 no.2
• /
• pp.371-383
• /
• 1997

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, a method of linear fuzzy regression analysis is described by interpreting the reliability of each input-output pair as its membership values. Next, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. The fuzzy neural network maps a crisp input vector to a fuzzy output. A cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is illustrated by computer simulations on numerical examples.

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

• The Journal of the Korea Contents Association
• /
• v.10 no.9
• /
• pp.455-468
• /
• 2010

The purpose of this study is to understand how and what kind of service quality impacts value of membership and customer satisfaction, and to analyze how value of membership and customer satisfaction influence on customer loyalty in order to apply the results of this study to the management level of individual companies. The valuable outcomes of the survey were 485 as a result of excluding both no response and non-trustful answers. The previous used questions of other researchers were modified and used in this study, and both SPSS 12.0 and AMOS 7.0 were in use as analytical softwares In this study, service quality of golf resort positively impacts membership's value, and service quality influences upon customer satisfaction in a positive way. The value of service quality heavily impacts customer satisfaction, and customer satisfaction positively influences upon customer loyalty. On the other hand, it is hardly find a remark between value of loyalty and customer's satisfaction.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.3
• /
• pp.304-310
• /
• 2009

In this paper, target extraction method in FLIR(forward-looking infrared) images based on fuzzy thresholding which used bi-modality and adjacency to determine membership value is proposed. The bi-modality represents how a pixel is classified into a part of target using distribution of pixel values in a local region, and The adjacency is a measure to represent how each pixel is far from the target region. First, membership value is calculated using above two measures, and then fuzzy thresholding is performed to extract the target. To evaluate performance of proposed target extraction method, we compare other segmentation methods using various FLIR tank image. Experimental results show that the proposed algorithm is a good segmentation performance.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.1
• /
• pp.36-43
• /
• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

• Proceedings of the Culinary Society of Korean Academy Conference
• /
• 2006.07a
• /
• pp.123-132
• /
• 2006

Although the numbers of the membership restaurants have increased steadily, the extant study has not been vigorously conducted. Therefore, it has been very hard to discuss in detail about the customer characteristics and the marketing strategies of these membership restaurants. The purpose of this study was to analyze the relationship among several factors, such as customer attitudes, relationship benefits, service convenience and customer loyalty. In order to accomplish the purpose, customers who are the members of M club at L hotel located in Busan were selected. The findings were as follows. First, there was a strong relationship between customer privacy and relationship benefits. Also, the relationship was found between customer value and relationship benefits. Second, service convenience and relationship benefits showed relationship with customer loyalty.

• Journal of KIISE:Databases
• /
• v.28 no.4
• /
• pp.545-557
• /
• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.