• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.518-523
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• 2015

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.23-27
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• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.1-12
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• 2001

In this paper, we investigate and analyze weight distribution of feed forward two-layer neural networks with a hidden layer in order to understand and improve time-consuming training process of neural networks. Generally, when a new problem is presented, neural networks have to be trained again without any benefit from the previous training process. In order to address this problem, training process is viewed as finding a solution point in the weight space and the distribution of solution points is analyzed. Then we propose to initialize neural networks using the information of the distribution of the solution points. Experimental results show that the proposed initialization using the weight distribution provides a better performance than the conventional one.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.520-520
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• 2023

제주도 동부 중산간 지역은 화산암으로 구성된 지하지질로 인해 지하수위의 변동폭이 크고 변동양상이 복잡하여 인공신경망(Artificial Neural Network, ANN) 모델 등을 활용한 지하수위의 예측이 어렵다. ANN에 적용되는 활성화함수에 따라 지하수의 예측성능은 달라질 수 있으므로 활성화함수의 비교분석 후 적절한 활성화함수의 사용이 반드시 필요하다. 본 연구에서는 5개 활성화함수(sigmoid, hyperbolic tangent(tanh), Rectified Linear Unit(ReLU), Leaky Rectified Linear Unit(Leaky ReLU), Exponential Linear Unit(ELU))를 제주도 동부 중산간지역에 위치한 2개 지하수 관정에 대해 비교분석하여 최적 활성화함수 도출을 목표로 한다. 또한 최적 활성화함수를 활용한 ANN의 적용성을 평가하기 위해 최근 널리 사용되고 있는 순환신경망 모델인 Long Short-Term Memory(LSTM) 모델과 비교분석 하였다. 그 결과, 2개 관정 중 지하수위 변동폭이 상대적으로 큰 관정은 ELU 함수, 상대적으로 작은 관정은 Leaky ReLU 함수가 지하수위 예측에 적절하였다. 예측성능이 가장 낮은 활성화함수는 sigmoid 함수로 나타나 첨두 및 최저 지하수위 예측 시 사용을 지양해야 할 것으로 판단된다. 도출된 최적 활성화함수를 사용한 ANN-ELU 모델 및 ANN-Leaky ReLU 모델을 LSTM 모델과 비교분석한 결과 대등한 지하수위 예측성능을 나타내었다. 이것은 feed-forward 방식인 ANN 모델을 사용하더라도 적절한 활성화함수를 사용하면 최신 순환신경망과 대등한 결과를 도출하여 활용 가능성이 충분히 있다는 것을 의미한다. 마지막으로 LSTM 모델은 가장 적절한 예측성능을 나타내어 다양한 인공지능 모델의 예측성능 비교를 위한 기준이 되는 참고모델로 활용 가능하다. 본 연구에서 제시한 방법은 지하수위 예측과 더불어 하천수위 예측 등 다양한 시계열예측 및 분석연구에 유용하게 사용될 수 있다.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.137-141
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• 1989

본 논문에서는 신경망 구조의 한 모델인 feed-forward multi-layered network에 역전파 학습(back-propagation learning) 기법을 이용하여 필기체 한글 숫자를 인식하고 그 가능성을 보였다. 문자 인식에 있어 입력 대상의 모양이 왜곡되거나, 대상의 크기 혹은 위치의 변화 등과 같은 잡음 (noise)에 대해서 정확히 대상을 인식하는 데는 대상의 구조 추출에 크게 관여되므로 한글의 구조 추출에 적합하다고 생각되는 bar mask 투사법을 제안하였다. 모델의 학습을 필기체 한글 숫자 16자의 입력 패턴과 타겟 ( target) 입력의 쌍을 이용해 학습시켰다. 또한, 모델의 인식 정도를 측정해 보기 위해 시험패턴을 적용하여 훈련된 패턴과 훈련되지 않은 패턴간의 인식률을 비교하여 보았다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.2
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• pp.1-7
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• 2011

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.171-174
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• 2007

A realization for FF-PID(Feed-Forward PID) controlling system with backward propagation algorithm and image pattern recognition is presented in this paper. The pattern recognition used backward propagation of nervous network is teaming. FF-PID is enhanced the response characteristic of moving image by using the controlling value which is output error for the target value of nervous system. In conclusion of experiment, the system is shown that the response is worked as 2.7sec that is enhanced round 15% in comparison with general difference image algorithm. The system is able to control a moving object with effect.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.499-505
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• 2010

The UAV(Unmanned Aerial Vehicle) which is remotely operating with long endurance in high altitude must have a very reliable propulsion system. The precise fault diagnostic system of the turboprop engine as a propulsion system of this type UAV can promote reliability and availability. This work proposes a diagnostic method which can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. It is found by evaluation examples that the proposed diagnostic method can detect well not only single type faults but also multiple type faults.