• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.250-253
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• 1998

This paper presents a multi-modal neural network composed of a preprocessing module and a multi-layer neural network module in order to enhance the nonlinear characteristics of neural network. The former module is based on spectral method using Chebyschev polynomials and transforms input data into spectra. The latter module identifies the system using the spectra generated by the preprocessing module. The omnibus numerical experiments show that the method is applicable to many a nonlinear dynamic system in the real world, and that preprocessing using Chebyschev polynomials reduces the number of neurons required for the multi-layer neural network.

• 한국컴퓨터정보학회논문지
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• 제4권3호
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• pp.67-72
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• 1999

신경회로망은 학습능력과 근사화 능력으로 말미암아 패턴인식 및 시스템제어분야에서 많이 사용되고 있으며, 입력층. 출력층. 하나 이상의 은닉층으로 구성된 네드워크이다. Elman 신경회로망은 J. Elman에 의해 제안되었으며. recurrent network의 형태로 구성되어 있다. Elman 신경회로망은 기존의 신경회로망에 context층을 새로 추가하여, 은닉층의 출력을 context층의 입력으로 피드백 하는 구조로 되어 있다. 본 논문에서는 새로운 형태의 Elman 신경회로망을 제안한다. 제안한 방식은 Elman 신경회로망을 변형한 형태로. 은닉층 뿐 만 아니라 출력층의 출력도 context층으로 피드백 하는 형태이다. 제안한 방식의 유용성을 확인하기 위해 multi target system에 적용한다. 시뮬레이션 결과는 제안한 방식이 기존의 신경회로망 및 Elman 신경회로망 보다 우수한 방식임을 보여 주고 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1565-1568
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• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with multi-layer neural network. The structure of modular neural network in researched by Jacobs and Jordan is selected in this paper. Modular network consists of several expert networks and a gating network which is composed of single-layer neural network or multi-layer neural network. We propose modular network structure using recurrent neural network, since the state of the whole network at a particular time depends on an aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.694-697
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• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with Multi-Layer Neural Network(MLNN). The structure of Modular Neural Network(MNN) in researched by Jacobs and jordan is selected in this paper. Modular network consists of several Expert Networks(EN) and a Gating Network(CN) which is composed of single-layer neural network(SLNN) or multi-layer neural network. We propose modular network structure using Recurrent Neural Network(RNN), since the state of the whole network at a particular time depends on aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• 한국군사과학기술학회지
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• 제4권2호
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• pp.189-201
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• 2001

For a design of multi-layer armor, the extensive full scale or sub-scale penetration test data are required. In generally, the collection of penetration data is in need of time-consuming and expensive processes. However, the application of numerical or analytical method is very limited due to poor understanding about penetration mechanics. In this paper, we have developed a neural network analyzer which can be used as a design tool for a new armor. Calculation results show that the developed neural network analyzer can predict relatively exact penetration depth of a new armor through the effective analysis of the pre-existing penetration database.

• 한국컴퓨터정보학회논문지
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• 제8권3호
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• pp.113-123
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• 2003

본 논문에서는 주식의 종가, 거래량 기술적 지표인 MACD(Moving Average Convergence Divergence) 값과 투자 심리선값을 입력 패턴으로 사용하여 개별 금융지표지수에 대한 매도, 중립 및 매수 시점 예측을 수행하는 신경망 모델이 제안된다. 이 모델은 역전파 알고리즘을 이용한 시계열 예측 기능과 균등다층연산 기능을 갖는다. 학습 데이터의 수가 각 범주들(매도, 중립, 매수)에 균일하게 분포되어 있지 않을 경우 기존의 신경망은 가장 우세한 범주의 예측 정확성만을 향상시키는 문제점을 가지고 있다. 따라서, 본 논문에서는 신경망의 구조, 동작, 학습 알고리즘에 대해 표현한 후 다른 범주의 예측 정확성도 향상시키기 위해 각 범주의 중요성을 이용하여 학습 데이터의 수를 조절하는 균등다층연산 방법을 제안한다. 실험 결과, 균등다층연산 신경망을 이용한 금융지표지수 예측 방법이 기존의 신경망을 이용한 금융지표지수 예측 방법 보다 각 범주에 대해 높은 정확성 비율을 보임을 확인할 수 있었다.

• 전자공학회논문지B
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• 제29B권1호
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• pp.58-66
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• 1992

This paper proposes a neuro-controller for position control of one-link flexible robot arm. Basically the controller consists of a multi-layer neural network and a conventional PD controller. Two controller are parallelly connected. Neural network is traind by the conventional error back propagation learning rules. During learning period, the weights of neural network are adjusted to minimize the position error between the desired hub angle and the actual one. Finally the effectiveness of the proposed approach will be demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.40-45
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• 2004

This work presents a boundary estimation approach in electrical impedance imaging for binary-mixture fields based on a parallel structured multi-layer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the parallel structure of multi-layer neural network. Results from numerical experiments shows that the proposed approach is insensitive to the measurement noise and has a strong possibility in the visualization of binary mixtures for a real time monitoring.

• 한국산업융합학회 논문집
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• 제16권3호
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• pp.61-68
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• 2013

Long-term runoff model can be used to establish the effective plan of water reources allocation and the determination of the storage capacity of reservoir. So this study aims at the development of monthly runoff model using artificial neural network technique. For this, it was selected multi-layer neural network(MLN) and radial basis function neural network(RFN) model. In this study, it was applied model to analysis monthly runoff process at the Wi stream basin in Nakdong river which is representative experimental river basin of IHP. For this, multi-layer neural network model tried to construct input 3, hidden 7, and output 1 for each number of layer. As the result of analysis of monthly runoff process using models connected with artificial neural network technique, it showed that these models were effective in the simulation of monthly runoff.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 SMICS 2004 International Symposium on Maritime and Communication Sciences
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• pp.1-5
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• 2004

In this paper, we propose a novel approach for evolving the architecture of a multi-layer neural network. Our method uses combined ART1 algorithm and Max-Min neural network to self-generate nodes in the hidden layer. We have applied the. proposed method to the problem of recognizing ID number in student identity cards. Experimental results with a real database show that the proposed method has better performance than a conventional neural network.