• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.132-139
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• 2002

In GMAW(Gas Metal Arc Welding) processes, bead geometry (penetration, bead width and height) is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage and travel speed, shielding gas, CTWD (contact-tip to workpiece distance) and so on. In this paper, welding process variables were selected as welding current, arc voltage and travel speed. And bead geometry was reasoned from the chosen welding process variables using neuro-fuzzy algorithm. Neural networks was applied to design FL(fuzzy logic). The parameters of input membership functions and those of consequence functions in FL were tuned through the method of learning by backpropagation algorithm. Bead geometry could be reasoned from welding current, arc voltage, travel speed on FL using the results learned by neural networks. On the developed inference system of bead geometry using neuro-furzy algorithm, the inference error percent of bead width was within $\pm$4%, that of bead height was within $\pm$3%, and that of penetration was within $\pm$8%. Neural networks came into effect to find the parameters of input membership functions and those of consequence in FL. Therefore the inference system of welding quality expects to be developed through proposed algorithm.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1798-1804
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• 2017

This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm's performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.367-372
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• 2012

This paper presents a development of an analog type magnetic positioning system and its positioning accuracy improvement using fuzzy inference system. As the magnetic positioning system used on a magnet-gyro guidance system for AGV(automatic guided vehicle), it measures a position of magnet embedded in floor of the work place. The existing product of the magnetic positioning system is very expensive in Korea because it is being sold in a foreign country exclusively. Moreover, the positioning accuracy of the product is low because it uses digital type unipolar hall sensors. Hence, we developed the magnetic positioning system by ourselves and improved the positioning accuracy of the developed magnetic positioning system using fuzzy inference system. For experiment, we used the analog type magnetic positioning system which we have developed, and compared the performance of the proposed method with the performance of the existing positioning method for the magnetic positioning system. In experimental results, we verified that the proposed method improved the positioning accuracy of the magnetic positioning system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.3
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• pp.57-66
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• 2001

This paper resents a control approach for designing a fuzzy-PI controller for a synchronous generator excitation and SVC system A combination of thyristor-controlled reactors and fixed capacitors (TCR-FC) type SVC is recognized as having the must fiexible control and high speed response, which has been widely utilized in power systems, is considered and designed to improve the response of a synchronous generator, as well as controlling the system voltage A Fuzzy-PI controller for SVC system was proposed in this paper. The PI gain parameters of the proposed Fuzzy-PI controller which is a special type of PI ones are self-tuned by fuzzy inference technique. It is natural that the fuzzy inference technique should be barred on humans intuitions and empirical knowledge. Nonetheless, the conventional ones were not so. Therefore, In this paper, the fuzzy inference technique of PI gains using MMGM(Min Max Gravity Method) which is very similar to humans inference procedures, was presented and allied to the SVC system. The system dynamic responses are examined after applying all small disturbance condition.

• Journal of Internet Computing and Services
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• v.4 no.2
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• pp.61-67
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• 2003

It has recently become known that the social issue of obesity, caused by increased caloric intake and lack of exercise, is a risk factor in the cause of various adult diseases. Above all, to prevent or cure obesity, we must accurately evaluate the degree of obesity, and we have used BML, WHR, and waist measurements for this purpose. In this paper, we propose an obesity evaluation system based on fuzzy inference using BML and waist measurement. For this purpose, we decided reasoning rule and membership function about BML and waist measurements. The inference result is presented in a descriptive sentence.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.125-132
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• 2011

Fire monitoring system detects a fire based on the values of various sensors, such as smoke, CO, temperature, or change of temperature. It detects a fire by comparing sensed values with predefined threshold values for each sensor. However, to prevent a fire it is required to predict a situation which has a possibility of fire occurrence. In this work, we propose a fire recognition system using a fuzzy inference method. The rule base is constructed as a combination of fuzzy variables derived from various sensed values. In addition, in order to solve generalization and formalization problems of rule base construction from expert knowledge, we analyze features of fire patterns. The constructed rule base results in an improvement of the recognition accuracy. A fire possibility is predicted as one of 3 levels(normal, caution, danger). The training data of each level is converted to fuzzy rules by FCM(fuzzy C-means clustering) and those rules are used in the inference engine. The performance of the proposed approach is evaluated by using forest fire data from the UCI repository.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.258-258
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• 2000

In this paper, we present the position control of pendulum system which is driven by a ultrasonic motor. Since the system's response is different for each initial position of pendulum, it is difficult to obtain the satisfiable control performance by using a neural network which is learned by off-line. To overcome this problem, we propose the multiple controller based on fuzzy inference system for ultrasonic motor. and controller is designed by neural network. The proposed method shows good performance for any initial positions and it's effectiveness is verified from experiments. We expect that ultrasonic motor can be used as actuators of robot's leg or manipulator.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.55-74
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• 2000

A knowledge-based with production rules is a representation of static knowledge of an expert. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a method to reflect the dynamic nature of a system when we make inferences with a knowledge-based system. This paper suggests a strategy of dynamic inference that can be used to take into account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy production rules. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [0,1]. Weights of relative importance of attributes in a rule are obtained by the AHP(Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected in an inference with fuzzy production systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.81-89
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• 1996

A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems.The proposed fuzzy modeling implements system structure and parameter identification in the eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.