• Title/Summary/Keyword: On-Line Learning Neural Networks

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on-line Modeling of Nonlinear Process Systems using the Adaptive Fuzzy-neural Networks (적응퍼지-뉴럴네트워크를 이용한 비선형 공정의 온-라인 모델링)

  • 오성권;박병준;박춘성
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.10
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    • pp.1293-1302
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    • 1999
  • In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

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Adaptive Control of Nonlinear Systems through Improvement of Learning Speed of Neural Networks and Compensation of Control Inputs (신경망의 학습속도 개선 및 제어입력 보상을 통한 비선형 시스템의 적응제어)

  • 배병우;전기준
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.43 no.6
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    • pp.991-1000
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    • 1994
  • To control nonlinear systems adaptively, we improve learning speed of neural networks and present a novel control algorithm characterized by compensation of control inputs. In an error-backpropagation algorithm for tranining multilayer neural networks(MLNN's) the effect of the slope of activation functions on learning performance is investigated and the learning speed of neural networks is improved by auto-adjusting the slope of activation functions. The control system is composed of two MLNN's, one for control and the other for identification, with the weights initialized by off-line training. The control algoritm is modified by a control strategy which compensates the control error induced by the indentification error. Computer simulations show that the proposed control algorithm is efficient in controlling a nonlinear system with abruptly changing parameters.

The development of semi-active suspension controller based on error self recurrent neural networks (오차 자기순환 신경회로망 기반 반능동 현가시스템 제어기 개발)

  • Lee, Chang-Goo;Song, Kwang-Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.8
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    • pp.932-940
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    • 1999
  • In this paper, a new neural networks and neural network based sliding mode controller are proposed. The new neural networks are an mor self-recurrent neural networks which use a recursive least squares method for the fast on-line leammg. The error self-recurrent neural networks converge considerably last than the back-prollagation algorithm and have advantage oi bemg less affected by the poor initial weights and learning rate. The controller for suspension system is designed according to sliding mode technique based on new proposed neural networks. In order to adapt shding mode control mnethod, each frame dstance hetween ground and vehcle body is estimated md controller is designed according to estimated neural model. The neural networks based sliding mode controller approves good peiformance throllgh computer sirnulations.

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Stabilization Position Control of a Ball-Beam System Using Neural Networks Controller (신경회로망 제어기을 이용한 볼-빔 시스템의 안정화 위치제어)

  • 탁한호;추연규
    • Journal of the Korean Institute of Navigation
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    • v.23 no.3
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    • pp.35-44
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    • 1999
  • This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

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A Development of Advanced Monitoring System for Resistance Spot Welding Machine using Neural Networks (신경회로망을 이용한 스폿용접의 개선된 감시 시스템의 개발)

  • Hong, Su-Dong;Kim, Sang-Hee;Eem, Jae-Kwon;Choi, Han-Go
    • Proceedings of the KIEE Conference
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    • 1997.07b
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    • pp.406-408
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    • 1997
  • This paper presents the new method of a nondestructive spot welding state inspection system using neural networks. The learning process of neural networks makes the inspection system to adapt the variable welding parameters. The inspecting process is working with on-line real-time after off-line learning process. This neural network based inspection system shows reliable results through the field test for variations of applied voltages, currents, and contact area of the welding electrode.

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Servo-Writing Method using Feedback Error Learning Neural Networks for HDD (피드백 오차 학습 신경회로망을 이용한 하드디스크 서보정보 기록 방식)

  • Kim, Su-Hwan;Chung, Chung-Choo;Shim, Jun-Seok
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.699-701
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    • 2004
  • This paper proposes the algorithm of servo- writing based on feedback error learning neural networks. The controller consists of feedback controller using PID and feedforward controller using gaussian radial basis function network. Because the RBFNs are trained by on-line rule, the controller has adaptation capability. The performance of the proposed controller is compared to that of conventional PID controller. Proposed algorithm shows better performance than PID controller.

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Constructing Neural Networks Using Genetic Algorithm and Learning Neural Networks Using Various Learning Algorithms (유전알고리즘을 이용한 신경망의 구성 및 다양한 학습 알고리즘을 이용한 신경망의 학습)

  • 양영순;한상민
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1998.04a
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    • pp.216-225
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    • 1998
  • Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

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A Study on the Parameter Estimation of an Induction Motor using Neural Networks (신경회로망을 이용한 유도전동기의 피라미터 추정)

  • 류한민;김성환;박태식;유지윤
    • Proceedings of the KIPE Conference
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    • 1998.07a
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    • pp.225-229
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    • 1998
  • If there is a mismatch between the controller programmed rotor time constant and the actual time constant of motor, the decoupling between the flux and torque is lost in an indirect rotor field oriented control. This paper presents a new estimation scheme for rotor time constant using artificial neural networks. The parameters of induction motor model organize 2 layer neural to be weight between neuron, which is proposed new in this paper. This method makes networks simple, so its brings not only the improvement in speed but simplification in calculation. Furthermore, it is possible to estimated rotor time constant real time through on-line learning without using off-line learning. The digital simulation and the experimental results to verify the effectiveness of the new method are described in this paper.

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Identification of suspension systems using error self recurrent neural network and development of sliding mode controller (오차 자기 순환 신경회로망을 이용한 현가시스템 인식과 슬라이딩 모드 제어기 개발)

  • 송광현;이창구;김성중
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.625-628
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    • 1997
  • In this paper the new neural network and sliding mode suspension controller is proposed. That neural network is error self-recurrent neural network. For fast on-line learning, this paper use recursive least squares method. A new neural networks converges considerably faster than the backpropagation algorithm and has advantages of being less affected by the poor initial weights and learning rate. The controller for suspension systems is designed according to sliding mode technique based on new proposed neural network.

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Controller Design using PreFilter Type Chaotic Neural Networks Compensator (Prefilter 형태의 카오틱 신경망 속도보상기를 이용한 제어기 설계)

  • Choi, Un-Ha;Kim, Sang-Hee
    • Proceedings of the KIEE Conference
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    • 1998.11b
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    • pp.651-653
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    • 1998
  • This thesis propose the prefilter type control strategies using modified chaotic neural networks #or the trajectory control of robotic manipulator. Since the structure of chaotic neural networks and neurons, chaotic neural networks can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis PUMA robot is designed by CNN. The CNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on- line learning and the performance is excellent. The CNN controller have much better controllability and shorter calculation time compared to the RNN controller. Another advantage of the proposed controller could be attached to conventional robot controller without hardware changes.

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