• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.179-179
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• 2000

An adaptive neural network compensator for stick-slip friction phenomena in servo systems is proposed to supplement the traditionally available position and velocity control loops for precise motion control. The neural network compensator plays a role of canceling the effect of nonlinear slipping friction force. This enables the mechatronic systems more precise control and realistic design in the digital computer. It was confirmed that the control accuracy is more improved near zero velocity and the points of changing the moving direction through numerical simulation

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.253-253
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• 2000

Each robot plays a role of its own behavior in dynamic robot-soccer environment. One of the most necessary conditions to win a game is control of robot movement. In this paper we suggest a win strategy using Cellular Neural Network to set optimal path and cooperative behavior, which divides a soccer ground into grid-cell based ground and has robots move a next grid-cell along the optimal path to approach the moving target.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.597-602
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• 2004

This study examined the forecasting of instant messinger's users choice using neural network. We used the statistical methods which were Logistic Regression, MDA(Multiple Discriminant Analysis), and ANN(Artificial Neural Network). In the result, the forecasting performance of the ANN was better than conventional model(Logistic Regression, MDA).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1769-1776
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• 2000

In this paper a new method is proposed to identify a modulation method in the case of unknown digitally modulated input signals. The proposed identification method is implemented with an artificial neural network which is based on characteristic feature extracted from the instantaneous amplitude the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 9 type signals (ASK2, FSK2, FSK4, PSK2, PSK4, PSK8, QAM8, QAM16) in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.617-624
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• 2000

The negative weight can be ofter seen in Hopfield neural network, which is difficult to implement negative conductance in circuits. Usually, the inverted output of amplifier is used to avoid negative resistors for expressing the negative weights in hardware implementation. However, there is some difference between using negative resistor and the inverted output of amplifier for representing the negative weight. This difference is discussed in this paper.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1066-1070
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• 2002

We propose a neural network based up-trend detector. An auto-associative neural network was trained with 'up-trend' data obtained from the KOSPI 200 future price. It was then used to predict an up-trend Simple investment strategies based on the detector achieved a two year return of $19.8\%$ with no leverage.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.11
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• pp.1038-1047
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• 2000

The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.499-506
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• 2004

The Unscented Kalman Filter is known to outperform the Extended Kalman Filter for the nonlinear state estimation with a significance advantage that it does not require the computation of Jacobian but EKF has a competitive advantage to the UKF on the performance time. We compare both algorithms on training the artificial neural network. The validation data set is used to estimate parameters which are supposed to result in better fitting for the test data set. Experimental results are presented which indicate the performance of both algorithms.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.11
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• pp.66-73
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• 1993

This paper presents an analog circuit implementation and experimental resuls of the Hopfield type neural network. The proposed architecture enables the reconfiguration betwewn feedback and feedforward networks and employs new circuit designs for the weight supply and storage, analog multilier, nd current-voltage converter, in order to achieve area efficiency as well as function al versatility. The layout design of the eight-neuron neural network is tested as an associative memory to verify its applicability to real world.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.52-58
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• 1995

A shared-weight neural network that performed classification based on the features extracted with the fuzzy morphological operation is introduced. Learning rules for the structuring elements, degree of membership, and weighting factors are also precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of-art for this problem.