• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• 대한전기학회논문지
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• 제41권3호
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• pp.274-280
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• 1992

In this paper, an Improved Error Back Propagation Algorithm is introduced, which avoids Network Paralysis, one of the problems of the Error Backpropagation learning rule. For this purpose, we analyzed the reason for Network Paralysis and modified the Activation Function Derivative of the standard Error Backpropagation Algorithm which is regarded as the cause of the phenomenon. The characteristics of the modified Activation Function Derivative is analyzed. The performance of the modified Error Backpropagation Algorithm is shown to be better than that of the standard Error Back Propagation algorithm by various experiments.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.345-347
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• 1998

스캐너를 가지고 이미지를 스캔하면 RGB 값을 얻는다. 이 RGB 값은 스캐너의 빛을 인지하는 소자들의 하드웨어적인 특성이 더해진 장치 의존적인 값이다. 그래서 RGB 값은 왜곡된 칼라 정보를 가지고 있다. 그러므로 칼라 보정을 하기 위해서는 장치 독립적이 값으로 변환해야 한다. 본 논문에서는 장치 독립적인 값을 구하기 위해서 칼라 샘플들을 XYZ로 계측한 값과 400nm에서 700nm 사이의 파장을 계측한 분광 반사값(Spectral reflectance value)을 가지고 스캐너의 칼라 보정을 구현하였다. 구현 방법으로는 신경회로망의 오차 역전파(Error Back Propagation) 알고리즘을 사용하였고 두 가지의 데이터를 가지고 실험했을 때의 결과와 장단점을 비교하였다.

• 한국경영과학회지
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• 제30권1호
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• 경영과학
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• 제18권2호
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• pp.25-38
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• 2001

The well-known back-propagation algorithm for multi-layered neural network has successfully been applied to pattern c1assification problems with remarkable flexibility. Recently. the multi-layered neural network is used as a powerful data mining tool. Nevertheless, in many cases with complex boundary of classification, the successful learning is not guaranteed and the problems of long learning time and local minimum attraction restrict the field application. In this paper, an Improved learning procedure of multi-layered neural network is proposed. The procedure is based on the generalized delta rule but it is particular in the point that the architecture of network is not fixed but enlarged during learning. That is, the number of hidden nodes or hidden layers are increased to help finding the classification boundary and such procedure is controlled by entropy evaluation. The learning speed and the pattern classification performance are analyzed and compared with the back-propagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.229-229
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• 2000

Conventional gain-tuning methods such as Ziegler-Nickels methods, have many disadvantages that optimal control ler gain should be tuned manually. In this paper, modified PID controllers which include self-tuning characteristics are proposed. Proposed controllers automatically tune the PID gains in on-1ine using neural networks. A new learning scheme was proposed for improving learning speed in neural networks and satisfying the real time condition. In this paper, using a nonlinear mapping capability of neural networks, we derive a tuning method of PID controller based on a Back propagation(BP)method of multilayered neural networks. Simulated and experimental results show that the proposed method can give the appropriate parameters of PID controller when it is implemented to DC Motor.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1992년도 추계학술대회
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• pp.47-50
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• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

• 동력기계공학회지
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• 제9권4호
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• pp.102-109
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• 2005

An attachment part of the construction equipment frequently claimed from the crack occurrence that takes especially at the bucket. therefore we execute the fatigue examination and changes the welding method at the same materials. we executed a fatigue crack propagation experiment and got the conclusions at the normal temperature and Frequency 10Hz. We carried out butt welding for structure steel of SM490A and make three kinds of specimen of different weld method each. The fatigue limit of CASE 1 was determined to the low than CASE 2, CASE 3. the CASE 2 putting the interval of the 2mm creates back plate and make fatigue limit to high. Bead shapes and weld surfaces shape influence on fatigue life of materials. Specially, the crack growth becomes starting point that gap of back-plate and boundary surface of bead. It is confirmed by fracture showing on this study.

• 에너지공학
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• 제6권1호
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• pp.68-76
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• 1997

본 논문에서는 단기 부하예측을 위하여 인공신경망 모형을 제안하였다. 본 논문에서 제안된 인공신경망의 학습알고리즘은 기존의 역전파 알고리즘 보다 효과적으로 학습수렴이 빠르며 모수결정과 초기가중치 값들에 대한 의존도가 낮은 동적 적응 학습알고리즘을 개발하여 단기 부하예측에 그 적용 가능성을 시험하였다.