• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 추계 학술논문 발표대회
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• pp.253-254
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• 2012

The purpose of this study is to suggest and verify high-strength concrete mix design model applying neural network theory, in order to minimize effort and time wasted by using trial and error method utill now. There are 7 input and 2 output to predict mix design. 40 data of mix design were learned with back-propagation algorithm. Then they are repeatedly learned back-propagation in neural network theory. Also, to verify predicted model, we analyzed and compared value predicted from 60MPa mix design with value measured by actual compressive strength test.

• 대한전기학회논문지
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• 제39권8호
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• pp.856-867
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• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.

• 대한기계학회논문집
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• 제18권9호
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• pp.2265-2271
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• 1994

With the improvement of standard of living, requirement for comfortable and quiet environment has been increased and, therefore, there has been a many researches for active noise reduction to overcome the limit of passive control method. In this study, active noise control is performed in a duct system using intelligent control technique which needs not decide the coefficients of high order filter and the mathematical modeling of a system. Back propagation algorithm is applied as an intelligent control technique and control system is organized to exclude the error microphone and high speed operational device which are indispensable for conventional active noise control techniques. Furthermore, learning is performed by organizing acoustic feedback model, and the effect of the proposed control technique is verified via computer simulation and experiment of active noise control in a duct system.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.839-842
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• 2000

A new and modified neural network model Is proposed for CT image reconstruction from four projection directions only. The model uses the Resilient Back-Propagation (Rprop) algorithm, which is derived from the original Back-Propagation, for adaptation of its weights. In addition to the error in projection directions of the image being reconstructed, the proposed network makes use of errors in pixels between an image which passed the median filter and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.

• 한국지반환경공학회 논문집
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• 제10권5호
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• pp.49-57
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• 2009

본 연구에서는 Levenberg-Marquardt(LM) 알고리즘 인공신경망을 통하여 지반공학 문제 중의 하나인 압축지수를 예측하였고, 예측된 결과는 현재 지반공학에 널리 사용되고 있는 Back Propagation(BP) 알고리즘 인공신경망의 예측 결과와 비교하여 LM 알고리즘의 지반공학 적용성을 평가하였다. 또한 두 알고리즘에 의한 예측치는 기존에 제안된 압축지수의 경험식들에 의하여 산정된 결과들과 비교를 통하여 예측결과의 정확성을 확인하였다. 경험식에 의한 압축지수의 산정치는 전반적으로 BP 알고리즘과 LM 알고리즘 인공신경망에 의한 예측치에 비하여 더 큰 오차를 나타냈다. LM 알고리즘에 의한 압축지수의 예측치는 BP 알고리즘의 예측치와 비교할 때 정확도는 비슷하나 수렴속도에서 더 좋은 결과를 보여 LM 알고리즘의 지반공학 적용성은 우수한 것으로 나타났다.

• 정보학연구
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• 제5권2호
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• pp.55-67
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• 2002

본 논문에서는 학습계수를 발생한 오차에 따라서 적응적으로 갱신할 수 있는 학습알고리즘에 은닉 노드의 수를 다양하게 변화시킬 수 있는 적응 역 전파(Back Propagation) 알고리즘을 제안하였다. 제안한 알고리즘은 국소점을 벗어날 수 있는 것으로 기대되고, 수렴환경에 알맞은 은닉 노드의 수를 설정할 수 있다. 모의실험에서는 두 가지의 학습패턴을 가지고 실험하였다. 하나는 X-OR 문제에 대한 학습과 또 다른 하나는 $7{\times}5$ 도트 영문자 폰트에 에 대한 학습이다. 두 모의실험에서 국소 점으로 안주할 확률은 감소하였다. 또한, 영문자 폰트 학습에서의 신경회로망은 기존의 역 전파 알고리즘과 HNAD 알고리즘에 비하여 약 41.56%~58.28%정도 학습효율이 향상됨을 고찰하였다.

• 대한전자공학회논문지TE
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• 제39권2호
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• pp.66-75
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• 2002

본 논문에서는 역전파 신경회로망의 학습능률을 향상시키기 위한 방법으로 발생한 오차에 따라서 학습파라미터와 은닉층의 수를 적응적으로 변경시킬 수 있는 적응 역 전파 학습알고리즘을 제안하였다. 제안한 알고리즘은 역전파 신경회로망이 국소점으로 수렴하는 문제를 해결할 수 있고 최적의 수렴환경을 만들 수 있다. 제안된 알고리즘을 평가하기 위하여 배타적 논리합, 3-패리티 및 7${\times}$5 영문자 폰트의 학습을 이용하였다. 실험결과, 기존에 제안된 알고리즘들에 비하여 국소점에 빠지게 되는 경우가 감소하였고 약 17.6%~64.7%정도 학습능률이 향상하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.