• 대한의용생체공학회:의공학회지
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• 제10권3호
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• pp.343-350
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• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.944-947
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• 1998

오늘날 항만에서의 컨테이너 처리를 하지 못하고 있는 가운데 자동화 처리를 위해서는 식별자의 인식이 가장 큰 문제점을 해결하기 위해 항만에서 컨테이너의 식별자 인식에 신경망 알고리즘의 하나인 back-propagation을 사용하여 기존의 식별자 인식 방법보다 신속하고 정확한 처리가 가능하도록 구현하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 통합자연과학논문집
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• 제1권3호
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• pp.221-229
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• 2008

본 논문에서는 기존의 개인 식별 방법의 한계를 해결하는 대안으로 떠오르고 있는 생체인식 기술 중 인식률이 뛰어나고 신뢰성 있는 홍채인식 시스템을 구현하고자 한다. 구현을 위하여 신호처리 분야에서 주로 사용되는 wavelet변환으로 계수 특징 값 추출을 하였으며, 인식률을 알아보기 위하여 신경망 기법을 이용하고자 한다. 그러나 신경망 기법에서 주로 사용되는 비선형 최적화기법인 Scale Conjugate Gradient는 최적화 문제점을 해결하기에는 수렴속도가 느리기 때문에 적합하지 않다. 따라서 본 논문에서는 기존 Scale Conjugate Gradient를 보완한 Levenberg-Marquardt Back-Propagation을 홍채인식에 적용하여 구현함으로써 인식율을 높이고자 한다. 적용한 알고리즘 구현으로 해의 수렴정도, 변수 벡터의 변화정도에 따라 크기를 적절히 변화시킴으로써 수렴속도를 개선하고, 효율성과 안정성을 동시에 얻을 수 있었다.

• 한국산업융합학회 논문집
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• 제7권2호
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2009년도 춘계학술대회 논문집
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• pp.357-360
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. In this research, we tried the back-propagation algorithm of artificial neural network to find an optimum solution in the cooling system design of injection mold. The cooling system optimization problem that was once solved by a response surface method with 4 design variables was solved by applying the back-propagation algorithm, resulting in a solution with a sufficient accuracy. Furthermore the number of training points was much reduced by applying the fractional factorial design without losing solution accuracy.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.477-483
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• 2004

One of the major paradigms for supervised learning in neural network community is back-propagation learning. The standard implementations of back-propagation learning are optimal under the assumptions of identical and independent Gaussian noise. In this paper, for regression function estimation, we introduce $\varepsilon-insensitive$ back-propagation learning algorithm, which corresponds to minimizing the least absolute error. We compare this algorithm with support vector machine(SVM), which is another $\varepsilon-insensitive$ supervised learning algorithm and has been very successful in pattern recognition and function estimation problems. For comparison, we consider a more realistic model would allow the noise variance itself to depend on the input variables.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.380-385
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• 2001

본 논문에서는 기존의 역전파 알고리즘(Back-propagation Algorithm)의 문제점인 지역 최소점(Local Minimum point) 빠질 위험을 제거함과 동시에 학습속도(learning-speed)를 빠르게 하기 위해서 모먼팀을 갖는 PID 역전파 알고리듬(PIDBPMT: PID Back-Propagation algorithm with Momentum Term)을 제안하여 모먼텀 항을 갖는 신경망 PID 제어기를 설계하였다. 그 제어기를 D.C 서보 모터를 구동원으로 하는 위치제어계에서 적용하여 시뮬레이션으로 그 성능을 검정하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1041-1044
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• 2000

The enhancement to the back-propagation algorithm presented in this paper has resulted from the need to extract sparsely connected networks from networks employing product terms. The enhancement works in conjunction with the back-propagation weight update process, so that the actions of weight zeroing and weight stimulation enhance each other. It is shown that the error measure, can also be interpreted as rate of weight change (as opposed to ${\Delta}W_{ij}$), and consequently used to determine when weights have reached a stable state. Weights judged to be stable are then compared to a zero weight threshold. Should they fall below this threshold, then the weight in question is zeroed. Simulation of such a system is shown to return improved learning rates and reduce network connection requirements, with respect to the optimal network solution, trained using the normal back-propagation algorithm for Multi-Layer Perceptron (MLP), Higher Order Neural Network (HONN) and Sigma-Pi networks.