• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.69-81
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• 1997

In this paper, first we propose an architecture of fuzzy neural networks with triangular fuzzy weights. The proposed fuzzy neural network can handle fuzzy input vectors. In both cases, outputs from the fuzzy network are fuzzy vectors. The input-output relation of each unit of the fuzzy neural network is defined by the extention principle of Zadeh. Also we define a cost function for the level sets(i. e., $\alpha$-cuts)of fuzzy outputs and fuzzy targets. Then we derive a learning algorithm from the cost function for adjusting three parameters of each triangular fuzzy weight. Finally, we illustrate our a, pp.oach by computer simulation examples.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.395-397
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• 1997

We have applied the neural network method for the neural networkmethod for the automatic scoring of the sleep stage. 17 features are extracted from the recorded EEG, EOG and EMG signals. These features are inputed to tile multilayer perceptron model. Neural network was trained with error-back propagation method. Results are compared with manual scoring of the experts, and show the possibility of application of automatic method in sleep stage scoring.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2873-2875
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• 2005

The optimal structure of the conventional Fuzzy Polynomial Neural Networks (FPNN)[3] depends on experience of designer. For the conventional Fuzzy Polynomial Neural Networks, input variable number, number of input variable, number of Membership Functions(MFs) and consequence structures are selected through the experience of a model designer iteratively. In this paper, we propose the new design methodology to find the optimal structure of Fuzzy Polymomial Neural Network by using Genetic Algorithms(GAs)[4, 5]. In the sequel, It is shown that the proposed Advanced Genetic Algorithms based Fuzzy Polynomial Neural Network(Advanced GAs-based FPNN) is more useful and effective than the existing models for nonlinear process. We used Medical Imaging System(MIS)[6] data to evaluate the performance of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.306-309
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• 1996

This paper presents a method of landmark recognition in indoor environments using a neural-network for an autonomous mobile robot. In order to adapt to image deformation of a landmark resulted from variations of view-points and distances, a multi-labeled template matching(MLTM) method and a dynamic area search method(DASM) are proposed. The MLTM is. used for matching an image template with deformed real images and the DASM is proposed to detect correct feature points among incorrect feature points. Finally a feed-forward neural-network using back-propagation algorithm is adopted for recognizing the landmark.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.698-701
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• 1996

A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.968-973
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• 2000

Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.402-408
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• 2010

Forecasting have qualitative and quantitative methods. Quantitative one analyze macro-economic factors such as the rate of exchange, oil price, interest rate and also predict the micro-economic factors such as sales and demands. Applying various statistical methods depends on the type of data. when data has seasonality and trend, Time Series analysis is proper but when it has casual relation, Regression analysis is good for this. Time Series and Regression can be used together. This study investigate artificial neural networks which is predictive technique for casual relation and try to compare the accuracy of forecasting between regression analysis and artificial neural network.

• The Journal of Information Systems
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• v.10 no.2
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• pp.151-164
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• 2001

현재의 항공사 기내식 수요예측 시스템으로는 항공기 운항의 지연이나 초과 주문으로 인한 손실 문제를 해결하기 힘든 것으로 알려져 있다. 이러한 문제를 해결하기 위해 본 연구에서는 항공기 기내식 시계열 자료만을 입력변수로 사용한 단순인공신경망모형(simple neural network model), 단순인공신경망모형에 전통적인 시계열 기법(본 연구에서는 지수 평활법)의 예측 결과를 입력변수로 추가한 혼합인공신경망모형(hybrid neural network model), 그리고 혼합인공신경 망 모형에 상관관계가 높은 다른 시계열 자료(본 논문에서는 유사 노선의 다른 항공기 기내식 시계열 자료)를 인공신경망의 입력변수로 추가시킨 하이퍼혼합인공신경망모형(hyper hybrid neural network model)을 새로운 항공기 기내식 수요예측 기법으로 제안하고, 이들 모형의 예측력을 비교 분석하였다. 분석 결과 하이퍼혼합인공신경망 모형의 예측력이 가장 우수한 것으로 나타나, 인공신경 망을 기반으로 한 수요예측에 있어 상관관계가 높은 다른 시계열 자료를 입력변수로 추가함으로써 인공신경망모형의 예측력을 개선시킬 수 있음을 알 수 있었다

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.130-132
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• 2005

This paper presents two ART-based neural networks for the identification of gas mixtures subject to the drift. A fuzzy ARTMAP neural network is used for classifying $H_2S$, $NH_3$ and their mixture gases including a reference gas. The other fuzzy ART neural network is utilized to detect the drift of a tin oxide gas sensor by tracking a cluster center of the reference gas. After detecting the drift, the previous cluster center of each gas is updated as much as the drift of the reference gas. By the simulations, the proposed method is shown to compensate the drift on-line without making many categories of target gases compared with the previous studies.