• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.999-1007
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• 2006

The Backpropagation learning algorithm is known to have slow and false convergence aroused from plateau and local minima. Many substitutes for backpropagation announced so far appear to pay some trade-off for convergence speed and stability of convergence according to parameters. Here, a new algorithm is proposed, which avoids some of those problems associated with the conventional backpropagation problems, especially with local minima, and gives relatively stable and fast convergence with low storage requirement. This is the separate learning algorithm in which the upper connections, hidden-to-output, and the lower connections, input-to-hidden, separately trained. This algorithm requires less computational work than the conventional backpropagation and other improved algorithms. It is shown in various classification problems to be relatively reliable on the overall performance.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.3-6
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• 2006

멀티레벨의 feed-forward 네트워크에 대한 학습 방법은 기울기 방법과 전역 최적화방법으로 나눌 수 있다. 역전파 또는 그 변형적인 방법들과 같은 기울기 하강 방법은 편리하기 때문에 여러 분야에서 다양하게 사용되고 있다. 하지만, 역전파와 관련된 가장 큰 문제는 지역 최소점에 빠진다는 것이다. 따라서 본 논문에서 기울기 하강 방법의 단순성을 침범하지 않고 지역 최소점을 극복할 수 있는 개선된 기울기 하강 방법을 제안한다. 제안하는 방법은 상위 연결과 하위연결을 분리하여 훈련하고 평가하기 때문에 이원적인 기울기 하강 방법이라 칭한다. 그렇기 때문에, 은닉층 유닛의 목표 값들은 하위 연결의 평가 툴로써 사용한다. 논문에서 제안하는 방법의 성능은 다양한 실험을 통해서 검증된다.