• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7277-7282
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• 2014

In this study, the gradient descent algorithm was used for FLC analysis and the algorithm was used to represent the effects of nonlinear parameters, which alter the antecedent and consequence fuzzy variables of FLC. The controller parameters choose the control variable by iteration for gradient descent algorithm. The FLC consists of 7 membership functions, 49 rules and a two inputs - one output system. The system adopted the Min-Max inference method and triangle type membership function with a 13 quantization level.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• Journal of the Ergonomics Society of Korea
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• v.23 no.2
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• pp.37-54
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• 2004

Fuzzy theory provides an intelligence treatment model for judgement about information when it needs a solution or a decision making about vague problems. Therefore, fuzzy theory is used for appropriate evaluation and decision on obscure information as human's emotion in human factors, In previous study, fuzzy membership function is defined for judgement infOlmation as human's emotion then ultimate results are deducted through fuzzy inference model. This method uses general CWTent through literature review or max, min and average as representative statics value about considering variables. But, this method makes away with nonlinear's or inegular's factors of human sensibility. Accordingly, application of this method leads to considerable loss of information in the ultimate evaluation. For that reason, this method has a limitation in objective evaluation of human factors. So, this study focuses on development of fuzzy membership function, which evaluates human's emotion or feeling accurately and objectively. We used the regression analysis and reasoned a fuzzy membership function about the relation of the variables. Then we verified the adequacy with the reliability through the experiment after this.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.259-262
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian menbership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.67-78
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• 1992

MODM (multiobjective decision-making) problem is very complex system for the analyst. The problem is more complex if the goals of each of the objective functions are expressed imprecisely. It requires suitable MODM method to deal with imprecisions. Therefore, we present a new interactive fuzzy decision making method for solving multiobjective nonlinear programming problems by assuming that the decision maker (DM) has imprecise goals that assume fuzzy linguistic variable for each of the objective functions. The imprecise goals of the DM are quantified by eliciting corresponding membership functions through the interactive with the DM out of six membership functions. After determining membership functions, in order to generate the compromise or satisficing solution which is .lambda.-pareto optimal, .lambda.-max problem is solved. The higher degree of membership is chosen to satisfy imprecise goals of all objective functions by combining the membership functions. Then, the values are the compromise or satisficing solution. On the basis of the proposed method, and interactive computer programming is written to implement man-machine interactive procedures. Our programming is a revised version of sequential unconstrained minimization technique. Finally, a numerical example illustrates various aspects of the results developed in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.5 s.98
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• pp.48-57
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• 1999

Applications of thresholding technique are based on the assumption that object and background pixels in a digital image can be distinguished by their gray level values. For the segmentation of more complex images, it is necessary to resort to multiple threshold selection techniques. This paper describes a new method for multiple threshold selection of gray level images which are not clearly distinguishable from the background. The proposed method consists of three main stages. In the first stage, a probability distribution function for a gray level histogram of an image is derived. Cluster points are defined according to the probability distribution function. In the second stage, fuzzy partition matrix of the probability distribution function is generated through the fuzzy clustering process. Finally, elements of the fuzzy partition matrix are classified as clusters according to gray level values by using max-membership method. Boundary values of classified clusters are selected as multiple threshold. In order to verify the performance of the developed algorithm, automatic inspection process of ball grid array is presented.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.109-118
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• 2007

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.156-166
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• 1995

This paper presents a bin picking method using a slit beam laser in which a robot recognizes all of the unoccluded objects from the top of jumbled objects, and picks them up one by one. Once those unoccluded objects are removed, newly developed unoccluded objects underneath are recognized and the same process is continued until the bin gets empty. To recognize unoccluded objects, a new algotithm to link edges on slices which are generated by the orthogonally mounted laser on the xy table is proposed. The edges on slices are partitioned and classified using convex and concave function with a distance parameter. The edge types on the neighborhood slices are compared, then the hamming distances among identical kinds of edges are extracted as the features of fuzzy membership function. The sugeno fuzzy integration about features is used to determine linked edges. Finally, the pick-up sequence based on MaxMin theory is determined to cause minimal disturbance to the pile. This proposed method may provide a solution to the automation of part handling in manufacturing environments such as in punch press operation or part assembly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1457-1464
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• 2011

In this paper, we go one step further in that the number of competitive layers is not determined by experience but can be determined by fuzzy control rules based on input pattern information. In our method, we design a set of membership functions and corresponding rules and used Max-Min reasoning proposed by Mamdani. Also, we use centroid method as a defuzzification. In experiment that has various patterns of English inputs, this new method works beautifully to determine the number of competitive layers and also efficient in overall accuracy as a result.