• Title/Summary/Keyword: neural network.

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The shortest path finding algorithm using neural network

  • Hong, Sung-Gi;Ohm, Taeduck;Jeong, Il-Kwon;Lee, Ju-Jang
    • 제어로봇시스템학회:학술대회논문집
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    • 1994.10a
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    • pp.434-439
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    • 1994
  • Recently neural networks leave been proposed as new computational tools for solving constrained optimization problems because of its computational power. In this paper, the shortest path finding algorithm is proposed by rising a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defined at first. To obtain this energy function, the concept of a vector-represented network is introduced to describe the connected path. Through computer simulations, it will be shown that the proposed algorithm works very well in many cases. The local minima problem of a Hopfield type neural network is discussed.

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Self-tuning optimal control of an active suspension using a neural network

  • Lee, Byung-Yun;Kim, Wan-Il;Won, Sangchul
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.295-298
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    • 1996
  • In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

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Neural Network Control Technique for Automatic Four Wheel Steered Highway Snowplow Robotic Vehicles

  • Jung, Seul;Lasky, Ty;Hsia, T.C.
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1014-1019
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    • 2005
  • In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.

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Width Prediction Model and Control System using Neural Network and Fuzzy in Hot Strip Finishing Mills (신경회로망과 퍼지 논리를 이용한 열간 사상압연 폭 예측 모델 및 제어기 개발)

  • Hwang, I-Cheal;Park, Cheol-Jae
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.4
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    • pp.296-303
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    • 2007
  • This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.

A Study on Real-time simulation using Artificial Neural Network (신경회로망을 이용한 실시간 시뮬레이션에 관한 연구 (원자력 발전소 중대사고를 중심으로))

  • Roh, Chang-Hyun;Jung, Kwang-Ho
    • Journal of Korea Game Society
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    • v.2 no.2
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    • pp.46-51
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    • 2002
  • In this study, a real-time simulation method for the phenomena, which are too complex to be simulated during real-time computer games, was proposed based on the neural network. The procedure of proposed method is to 1) obtain correlation data between input parameters and output parameters by mathematical modeling, code analyses, and so on, 2) train the neural network with the correlation data, 3) and insert the trained neural network in a game program as a simulation module. For the case that the number of the input and output parameters is too high to be analyzed, a method was proposed to omit parameters of little importance. The method was successfully applied to severe accidents of nuclear power plants, reflecting that the method was very effective in real time simulation of complex phenomena.

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Face Detection Algorithm Using Pulse-Coupled Neural Network in Color Images (컬러영상에서 Pulse-Coupled Neural Network를 이용한 얼굴 추출 알고리즘)

  • 임영완;나진희;최진영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.292-296
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    • 2004
  • 본 논문에서는 컬러영상에서 Pulse-Coupled Neural Network를 이용한 얼굴 추출 알고리즘의 성능을 향상시키는 방법에 대하여 논의하였다. 색상정보를 이용한 얼굴 추출 알고리즘은 얼굴의 기울어진 정도나 크기 둥에 영향을 받지 않으므로, 형태정보를 이용한 얼굴 추출 알고리즘에 비해 비교우위를 가진다. 그러나 조명의 변화가 심하거나, 피부색과 유사한 배경이 포함되어 있을 경우 적절한 성능을 내기 어렵다. 이러한 문제점들을 해결하기 위해 본 연구에서는 넓은 피부색 영역을 추출하고, Pulse-Coupled Neural Network를 통해 공간적으로 근접한 피부색 동종영역을 분리해 내는 방법을 사용하였다. 그리고 피부색 영역에 해당하는 픽셀들이 다른 영역들에 비해 큰 값을 갖도록 하여, Pulse-Coupled Neural Network의 linking coefficient를 보다 쉽게 결정하도록 하였다.

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A Study on a Neural Network-Based Feed Identification Method in Crude Distillation Unit (신경회로망을 이용한 원유정제공정에서의 조성식별방법에 관한 연구)

  • 이인수;이현철;박상진;이의수
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.5
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    • pp.449-458
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    • 2000
  • In this paper, we propose a feed identification method using neural network to predict feed in crude distillation unit. The proposed FINN(feed identifier by neural network) is functionally composed of two modes-training mode and prediction mode. Also, we implement a neural network-based soft sensor system using Borland C++(3.0) Builder. The effectiveness of the proposed neural network-based feed identification method is shown by simulation results.

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EEG Signal Prediction by using State Feedback Real-Time Recurrent Neural Network (상태피드백 실시간 회귀 신경회망을 이용한 EEG 신호 예측)

  • Kim, Taek-Soo
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.1
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    • pp.39-42
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    • 2002
  • For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

Robust control of PID control system using Neural network-Supervisory controller (신경망-관리 제어기를 이용한 PID 제어 시스템의 강인제어)

  • Ji, Bong-Chul;Choi, Seok-Ho;Park, Wal-Seo;Ryu, In-Ho;Choi, Hyeon-Seob
    • Proceedings of the KIEE Conference
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    • 1999.07b
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    • pp.791-793
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    • 1999
  • In this paper, neural network-supervisory control method is proposed to minimize the effect of system uncertainty by load change and disturbance in the PID control system. In the proposed method, PID controller performs main control action by performing control within constraint error. And neural network-supervisory controller performs control action when error reaches the boundary of constraint error. Combining neural network-supervisory controller to guarantee the stability into PID control system, the resulting PID control system is expected to show better performance in the system with load change and disturbance. Simulation applying PID controller and neural network-supervisory controller showed excellence of proposed method.

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Neural-Tabu algorithm in optimal routing considering reliability indices (신뢰도 지수를 고려한 계통의 Neural-Tabu 알고리즘을 이용한 최적 전송 경로 결정)

  • Shin, Dong-Joon;Jung, Hyun-Soo;Kim, Jin-O
    • Proceedings of the KIEE Conference
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    • 1999.07c
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    • pp.1245-1247
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    • 1999
  • This paper describes the optimal reconfiguration of distribution network. The optimal routing of distribution network should provide electricity to customers with quality, and this paper shows that optimal routing of distribution network can be obtained by Neural-Tabu algorithm while keeping constraints such as line power capacity, voltage drop and reliability indices. The Neural-Tabu algorithm is a Tabu algorithm combined with Neural network to find neighborhood solutions. This paper shows that not only the loss cost but also the reliability cost should be considered in distribution network reconfiguration to achieve the optimal routing.

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