• Title/Summary/Keyword: Multilayer Neural Network

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Nonlinear system control using neural network guaranteed Lyapunov stability (리아프노브 안정성이 보장되는 신경회로망을 이용한 비선형 시스템 제어)

  • Seong, Hong-Seok;Lee, Kwae-Hui
    • Journal of Institute of Control, Robotics and Systems
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    • v.2 no.3
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    • pp.142-147
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    • 1996
  • In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

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Control of Nonlinear System with a Disturbance Using Multilayer Neural Networks

  • Seong, Hong-Seok
    • Transactions on Control, Automation and Systems Engineering
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    • v.2 no.3
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    • pp.189-195
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    • 2000
  • The mathematical solutions of the stability convergence are important problems in system control. In this paper such problems are analyzed and resolved for system control using multilayer neural networks. We describe an algorithm to control an unknown nonlinear system with a disturbance, using a multilayer neural network. We include a disturbance among the modeling error, and the weight update rules of multilayer neural network are derived to satisfy Lyapunov stability. The overall control system is based upon the feedback linearization method. The weights of the neural network used to approximate a nonlinear function are updated by rules derived in this paper . The proposed control algorithm is verified through computer simulation. That is as the weights of neural network are updated at every sampling time, we show that the output error become finite within a relatively short time.

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Robust control of nonlinear system using multilayer neural network (다층 신경회로망을 이용한 비선형 시스템의 견실한 제어)

  • 성홍석;이쾌희
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.34S no.9
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    • pp.41-49
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    • 1997
  • In this paper, we describe the algorithm which controls an unknown nonlinear system with disturbance a using multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate an unknown nonlinear system by using of multilayer neural netowrk. WE include a disturbance among the modelling error, and the weight-update rule of multilayer neural network is derived to satisfy Laypunov stability. The whole control system constitutes controller using the feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

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An Efficient Fault-diagnosis of Digital Circuits Using Multilayer Neural Networks (다층신경망을 이용한 디지털회로의 효율적인 결함진단)

  • 조용현;박용수
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.1033-1036
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    • 1999
  • This paper proposes an efficient fault diagnosis for digital circuits using multilayer neural networks. The efficient learning algorithm is also proposed for the multilayer neural network, which is combined the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The fault-diagnosis system using the multilayer neural network of the proposed algorithm has been applied to the parity generator circuit. The simulation results shows that the proposed system is higher convergence speed and rate, in comparision with system using the backpropagation algorithm based on the gradient descent.

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Quantitative Analysis for Biomass Energy Problem Using a Radial Basis Function Neural Network (RBF 뉴럴네트워크를 사용한 바이오매스 에너지문제의 계량적 분석)

  • Baek, Seung Hyun;Hwang, Seung-June
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.36 no.4
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    • pp.59-63
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    • 2013
  • In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

System Identification Using Gamma Multilayer Neural Network (감마 다층 신경망을 이용한 시스템 식별)

  • Go, Il-Whan;Won, Sang-Chul;Choi, Han-Go
    • Journal of the Institute of Convergence Signal Processing
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    • v.9 no.3
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    • pp.238-244
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    • 2008
  • Dynamic neural networks have been applied to diverse fields requiring temporal signal processing. This paper presents gamma neural network(GAM) to improve the dynamics of multilayer network. The GAM network uses the gamma memory kernel in the hidden layer of feedforword multilayer network. The GAM network is evaluated in linear and nonlinear system identification, and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of its performance. Experimental results show that the GAM network performs better with respect to the convergence and accuracy, indicating that it can be a more effective network than conventional multilayer networks in system identification.

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Pattern Classification Method using SOFM and Multilayer Neural Network (SOFM과 다층신경회로망을 이용한 패턴 분류 방식)

  • 박진성;공휘식;이현관;김주웅;엄기환
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2002.11a
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    • pp.296-300
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    • 2002
  • We proposed a method of a pattern classification using unsupervised teaming rules, SOFM, and supervised teaming rules, Multilayer neural network. Establish result that classify and get input pattern using SOFM by initial weighting vector of Multilayer neural network and target value. Got superior Performance as result that do simulation about face image to confirm usefulness of way that propose.

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Nonlinear system control using neural network (신경회로망을 이용한 비선형 시스템 제어)

  • 성홍석;이쾌희
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.7
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    • pp.32-39
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    • 1996
  • In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural netowrk can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural netowrk. The weights on the hidden layer of multilayer neural network are updated by gradient method. The weight-update rule on the output layer is derived to satisfy lyapunov stability. Also, we obtain secondary controller form deriving step. The global control system consists of controller using feedback linearization method and secondary controller is order to satisfy layapunov stability. The proposed control algorithm is verified through computer simulation.

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Dynamic Control of Robot Manipulators Using Multilayer Neural Networks and Error Backpropagation (다층 신경회로 및 역전달 학습방법에 의한 로보트 팔의 다이나믹 제어)

  • 오세영;류연식
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.39 no.12
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    • pp.1306-1316
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    • 1990
  • A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

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Implementation of Stable Adaptive Neural Networks for Feedback Linearization (피이드백 선형화를 위한 안정한 적응 신경회로망 구현)

  • Kim, Dong-Hun;Yang, Hai-Won
    • Proceedings of the KIEE Conference
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    • 1996.11a
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    • pp.58-61
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    • 1996
  • For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

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