• 한국항만학회지
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• 제12권2호
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• pp.225-232
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• 1998

Todays, the efficient management of container has not been realized in container terminal, because of the excessive quantity of container transported and manual system. For the efficient and automated management of container in terminal, the automated container identifier recognition system in terminal is a significant problem. However, the identifier recognition rate is decreased owing to the difficulty of image preprocessing caused the refraction of container surface, the change of weather and the damaged identifier characters. Therefore, this paper proposes more accurate system for container identifier recognition as suggestion of LSPRD(Line-Scan Proper Region Detection) for stronger preprocessing against external noisy element and MBP(Momentum Back-Propagation) neural network to recognize the identifier.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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• pp.293-295
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• 1993

In this paper, Neural-Network Identifier that has time-delay element, error limit and small weighting factor is proposed. A proposed identifier has good performance to identify non-linear system with noise. To test the effectiveness of the algorithm presented above, the simulation for output tracking of non-linear system is implemented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.776-778
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• 1999

In this paper, identifier for thermal storage system using multi-layer feedforward neural network (MFNN) is designed. It is very difficult to control thermal storage system, since thermal storage system is nonlinear and its time constant is very large. Thus, in the MFNN, delta-bar-delta algorithm for high running speed and 2-bit status input are used. Also hardware using microprocessor for identifier is developed. The experimental results indicate that the proposed method can predict temperature more accurately.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.1297-1300
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• 1998

In this paper, we discuss the identification of a chaotic system using chaotic neural networks. Because of selfconnections in neuron itself and interconnections between neurons, chaotic neural networks identifiers show good performance in highly nonlinear dynamics such as chaotic system. Simulation results are presented to demonstrate robustness of chaotic neural networks identifier.

• Transactions on Control, Automation and Systems Engineering
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• 제2권3호
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• 대한전기학회논문지:전력기술부문A
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• 제48권10호
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• pp.1231-1238
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• 1999

This paper presents an approach for the optimal heating load Identification using Diagonal Recurrent Neural Networks(DRNN). In this paper, the DRNN captures the dynamic nature of a system and since it is not fully connected, training is much faster than a fully connected recurrent neural network. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself. In this study, A dynamic backpropagation (DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. Delta-bar-delta learning method is an empirical method to adapt the learning rate gradually during the training period in order to improve accuracy in a short time. The simulation results based on experimental data show that the proposed model is superior to the other methods in most cases, in regard of not only learning speed but also identification accuracy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.62-64
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• 1996

Neural network as a controller of a nonlinear system and a system identifier has been studied during the past few years. A well trained neural network identifier can be used as a system predictor. We proposed the method to design multi-step ahead predictor and multi-step predictive controller using neural network. We used the input and out put data of B system to train the NNP and used the forecasted approximat system output from NNP as B input of NNC. In this paper we used two-step ahead predictive controller to test B heating controll system and compared with PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1017-1021
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• 1991

This paper is to explore the potential use of the modeling capacity of neural networks for control applications. The tasks are carried out by two neural networks which act as a plant identifier and a system controller, respectively. Using information stored in the identification network control action has been developed. Without supervising control signals are generated by a gradient type iterative algorithm.

• 한국통신학회논문지
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• 제21권1호
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• pp.291-299
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• 1996

이 논문에서는 다층 구조 고차 신경회로망을 이용하여 로봇 메니퓰레이터와 같이 관성이 크고 복잡한 특성을 갖는 시스템을 효과적으로 동정화하는 새로운 방법을 제안한다. 로봇 메니퓰레이터의 위치 및 속도와 미리 정해준 기준 점들 사이의 차이를 나타내는 특정 성능 지수 함수를 최소화하는 방법을 이용하여 동정화 과정에 필요한 신경회로망의 학습에 이용되는 입력 데이터를 설계하는 방법을 설명한다. 사람의 팔과 같이 비교적 큰 관성을 갖는 2축 로봇 매니퓰레이터를 이용한 컴퓨터 시뮬레이션으로부터 제안된 방법이 복잡한 특성을 갖는 시스템의 동정화에 필요한 입력 데이터를 효과적으로 설계할 수 있음을 보인다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1021-1024
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• 1993

System identification is a major component for a control system. In biosystems, which is nonlinear and dynamic, precise identification would be very helpful for implementing a control system. It is difficult to precisely identify such non-linear systems. The measurable data on products from 2,3-butanediol fermentation could not be included in a process model based on kinetic approach. Meanwhile, a predictive capability is required in developing a control system. A neural network (NN) dynamic identifier with a by/(1＋ t ) transfer function was therefore designed being able to predict this fermentation. This modified inverse NN identifier differs from traditional models in which it is not only able to see but also able to predict the system. A moving window, with a dimension of 11 and a fixed data size of seven, was properly designed. One-step ahead identification/prediction by an 11-3-1 BPNN is demonstrated. Even under process fault, this neural network is still able to perform several-step ahead prediction.