• Journal of Information Processing Systems
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• v.15 no.3
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• pp.655-669
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• 2019

Supplier evaluation is of great significance in green supply chain management. Influenced by factors such as economic globalization, sustainable development, a holistic index framework is difficult to establish in green supply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertainty and incompleteness and the index weight is variable. To solve these problems, an index framework is established after comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is put forward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework. The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paper is feasible and effective.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.161.4-161
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• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.1-10
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• 1997

In this paper, for useful administration of the data which have ambiguities meaningfully and hard to treat, a new relation database model using an integrated fuzzy sets and rough sets relational database one. After proposing Fuzzy-rough relational database model on the base of integrated Fuzzy and Rough sets, Application of the examples of arithmetic is analyzed through the Access DBMS and the visual basic by composing and representing database based on fuzzy and rough sets which are characterized as fuzzy sets and rough sets on Pentium computer(166Mhz). This paper was induced to reduce the data incompleteness.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.75-80
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• 1998

Process information is known to have the continuous distribution in many manufacturing processes. Generalized p-chart has been developed for controlling processes by classifying the information characteristics into several groups. But it is improper to describe continuous processes with the classified process informal ion, which is based on the classical set concept. Fuzzy control chart, has been developed for the control of linguistic data, but it is also based on the dichotomous notion of classical set theory. In this paper, fuzzy sampling method is studied in order to process the uncertain data properly. The method is incorporated with the fuzzy control chart. Statistical characteristics of the fuzzy representative value are utilized to device the fuzzy-statistical control chart. The fuzzy-statistical control chart is compared with the generalized p-chart and both the sensitivity to the process information distribution change pared robustiness against the noise on the process information of the fuzzy-statistical control chart are shown to be superior.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.22-24
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• 2002

Computation with fuzzy numbers is a prospective branch of a fuzzy set theory regarding the data processing applications. In this paper we consider an open problem about distributivity of fuzzy Quantities based on the extension principle suggested by Mares (1997). Indeed, we show that the distributivity on the class of fuzzy numbers holds and min-norm is the only continuous f-norm which holds the distributivity under f-norm based fuzzy arithmetic operations.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.301-310
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• 2000

An autonomous tractor system was developed and its performance was evaluated. The system consisted of a tractor system of and a remote control station. The tractor and the remote control station communicated each other via wireless modems. The tractor had a DGPS(differential global positioning system), sensors, a controller and a modem. The DGPS collected position data and the tractor status was estimated. The information of tractor status and sensors was transferred to the remote control station. Then, the control station determined the control data such as steering angles using a fuzzy controller. The fuzzy controller used the information from the DGPS, sensors, and GIS(geographic information system) data. The control data were obtained by remote signal processing at the control station The control data for autonomous operation were transferred to the tractor controller. The performances of an autonomous tractor were evaluated for various speeds, different initial positions and different initial headings. About 1.3 seconds of time lag was occurred in transferring the tractor status data and the control data. Compensation the time lag, about 27cm deviation was observed at the speed of 0.5m/s and 37cm at the speed of 1m/s. Error caused mainly by the time lag and it would be reduced by developing a full-duplex radio module for controlling the remote tractor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.13-16
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• 2000

In fuzzy expert systems or database systems that have huge volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environment. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy rules or data, the parallel fuzzy inference algorithm extracts effective parallel ism and achieves a good speed factor.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.541-546
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• 2006

Data Mining is the process of finding hidden patterns inside a large data set. Cluster analysis has been used as a popular technique for data mining. It is a fundamental process of data analysis and it has been Playing an important role in solving many problems in pattern recognition and image processing. If fuzzy cluster analysis is to make a significant contribution to engineering applications, much more attention must be paid to fundamental decision on the number of clusters in data. It is related to cluster validity problem which is how well it has identified the structure that Is present in the data. In this paper, we design an adaptive data mining model using fuzzy performance measures. It discovers clusters through an unsupervised neural network model based on a fuzzy objective function and evaluates clustering results by a fuzzy performance measure. We also present the experimental results on newsgroup data. They show that the proposed model can be used as a document classifier.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.5
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• pp.1-12
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• 2000

In this paper we propose a design method of the adaptive fuzzy controller for autonomous navigation of mobile robots based on the fuzzy theory. We present two improvements. First, unnecessary rules in the fuzzy inference process make data processing time increase. We reduce this data processing time by generating suitable fuzzy inference rules and membership functions according to the current state of a mobile robot. It is implemented with the clustering method using input and output data pairs, and then it is possible for a mobile robot to navigate in shorter processing time with less fuzzy inference rules. Second, existing algorithms used fixed membership functions of input and output variables, hence converged slowly. We improve convergence time via scaling membership functions generated by the clustering method. To evaluate and compare the performance of the proposed method with the existing fuzzy navigation controller, computer simulations and navigation experiments of a mobile robot are Presented.