• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.219-222
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• 1997

Due to the unpredictability and irregularity, the behaviors of chaotic systems are considered as undesirable phenomena to be avoided or controlled. Thus in this paper, to control systems showing chaotic behaviors, a direct adaptive control method using a radial basis function network (RBFN) as an excellent alternative of multi-layered feed-forward networks is presented. Compared with an indirect scheme, a direct one does not need the estimation of the controlled process and gives fast control effects. Through simulations on the two representative continuous-time chaotic systems, Duffing and Lorenz systems, validity of the proposed control scheme is shown.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1486-1491
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• 1999

This paper presents a method of recognizing high impedance fault(HIF) of electrical power systems and classifying fault patterns based on chaos attractors. Two dimensional chaos attractors are reconstructed from neutral point current waveforms. Reliable features for HIF pattern classification are obtained from the chaos attractors. Radial basis function network, trained with two types of HIF data generated by the electromagnetic transient program and measured form actual faults. The RBFN successfully classifies normal and the three types of fault patterns according to the features generated from the chaos attractors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.221-224
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• 2001

In this paper, we propose the initial optimized structure of the Radial Basis Function Network which is more simple in the part on the structure and converges more faster than Neural Network with the analysis method using Time-Frequency Localization and genetic algorithm. When we construct the hidden node with the Radial Basis Function whose localization is similar with an approximation target function in the plane of the Time and Frequency, we have initial structure of RBFN, After that, we evaluate the parameters of RBF in the network and the parameters needed for the network is more a few. Finally, we make a good decision of the initial structure having an ability of approximation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.163.1-163
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• 2001

In this paper, we use time-frequency localization analysis method to analize the target function and the area of the target space. When we analize the function with the time and frequency axis simultaneously, the characteristic of the function is shown more precisely and the area is covered by a certain block. After we analize the target function in the time-frequency space, we can decide the activation functions and compose the hidden layer of the RBFN by choosing the radial basis function which can represent the characteristic of the target function, RBFN made by this method, designs the good structure proper to the target problem because we can decide the number of hidden node first.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.161-172
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• 2016

The use of a chemical sensor array can help discriminate between chemicals when comparing one sample with another. The ability to classify pattern characteristics from relatively small pieces of information has led to growing interest in methods of sensor recognition. A variety of pattern recognition algorithms, including the adaptive radial basis function network (RBFN), may be applicable to gas and/ or odor classification. In this paper, we provide a broad review of approaches for various types of gas and/or odor identification techniques based on RBFN and drift compensation techniques caused by sensor poisoning and aging.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.67-70
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• 1998

There were many cases to apply artificial intelligence to medicine. In this paper, we present the prediction model of the development of the NIDDM(noninsulin-dependent diabetes mellitus). It is not difficult that doctor diagnose patient as DM(diabetes mellitus). However NIDDM is usually developmented later on 40 years old and symptom appeares gradually. So screening test or prediction model is needed absolutely. Our model predicts development of NIDDM with still normal data 2 year ago. Prediction models developed are both MLP(multilayer perceptron) with backpropagation training and RBFN(radial basis function network). Performance of both models were evaluated with likelihood ratio. MLP was about two and RBFN was about three. We expect that models developed can prevent development of DM and utilize normal data.

• Proceedings of the KIEE Conference
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• summer
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• pp.202-204
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• 2004

This paper presents a neural network technology for the detection and classification of the various types of power quality disturbances. Power quality phenomena are short-time problems and of many varieties. Particularly, the transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting ·md classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. We test two neural network and compare the results of Backpropagation Neural (BPN) network with Radial basis function network (RBFN). RBFN is more useful to detect and classify than BPN. The configuration of the hardware of PQ-DAS and some case studies are described.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.84-91
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• 2005

For long time, the takeoff and landing control of airship was worked by human handling. With the development of the autonomous control system, the exact controls during the takeoff and landing were required and lots of methods and algorithms were suggested. This paper presents the result of airship take-off and landing by buoyancy control using air ballonet volume change and performance control of pitch angle for stable flight within the desired altitude. For the complexity of airship's dynamics, firstly, simple PID controller was applied. Due to the various atmospheric conditions, this controller didn't give satisfactory results. Therefore, new control method was designed to reduce rapidly the error between designed trajectory and actual trajectory by learning algorithm using an artificial neural network. Generally, ANN has various weaknesses such as large training time, selection of neuron and hidden layer numbers required to deal with complex problem. To overcome these drawbacks, in this paper, the RBFN (radial basis function network) controller developed. The weight value of RBFN is acquired by learning which to reduce the error between desired input output through and airship dynamics to impress the disturbance. As a result of simulation, the controller using the RBFN is superior to PID controller which maximum error is 15M.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.1-6
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• 2001

In this paper, we propose the initial optimized structure of the Radial Basis Function Network(RBFN) which is more simple in the part of the structure and converges more faster than Neural Network. For this, we use the analysis method using time frequency localization and we can decide the initial structure of the RBFN suitable for the given problem. When we compose the hidden nodes with the radial basis functions whose localization are similar with the target function in the plane of the time and frequency, we can make a good decision of the initial structure having an ability of approximation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.159-162
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• 2004

플라즈마 공정 모델 개발에 역전파 신경망이 가장 많이 응용되고 있으나, 관여하는 다수의 학습인자로 인해 그 최적화가 매우 어렵다. Radial basis function network (RBFN)은 관여하는 학습인자의 수가 적어 그 최적화가 상대적으로 용이하지만, 두인자의 다양한 조합에 의해 RBFN의 예측성능이 상당히 영향을 받을 수 있다. 본 연구에서는 학습인자 상호간의 작용을 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 최적화하는 기법을 소개한다. 제안하는 알고리즘을 광도파로 제작을 위해 수행한 실리카 식각공정 데이터에 적용하여 평가하였다. 평가에 이용된 식각 응답은, 실리카 식각률, aluminum (Al) 식각률, Al 선택비, 그리고 실리카 프로파일 각도이다. 최적화한 모델은 종래의 모델과 비교하였으며, 그 향상도는 실리카 식각률, Al 식각률, Al 선택비, 그리고 실리카 프로파일 각도에 대해서 각 기 0.8%, 32.4%, 20.3%, 1.3% 등이었다. Al 식각률과 선택비에 대해서 예측성능은 상당이 향상되었다.