• 제목/요약/키워드: Input predictor

검색결과 83건 처리시간 0.029초

A comparison of neural networks to ols regression in process/quality control applications

  • Nam, Kyungdoo;Sanford, Clive C.;Jayakumar, Maliyakal D.
    • 경영과학
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    • 제11권2호
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    • pp.133-146
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    • 1994
  • This study compares the performance of neural networks and ordinary least squares regression with quality-control processes. We examine the applicability of neural networks because they do not require any assumptions regarding either the functional from of the underlying process or the distribution of errors. The coefficient of determination($R^2$), mean absolute deviation(MAD), and the mean squared error(MSE) metrics indicate that neural networks are a viable and can be a superior technique. We also demonstrate that an assessment of the magnitude of the neural notwork input layer cumulative weights can be used to determine the relative importance of predictor variables.

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전처리과정을 갖는 시계열데이터의 퍼지예측 (A Fuzzy Time-Series Prediction with Preprocessing)

  • 윤상훈;이철희
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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    • pp.666-668
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    • 2000
  • In this paper, a fuzzy prediction method is proposed for time series data having uncertainty and non-stationary characteristics. Conventional methods, which use past data directly in prediction procedure, cannot properly handle non-stationary data whose long-term mean is floating. To cope with this problem, a data preprocessing technique utilizing the differences of original time series data is suggested. The difference sets are established from data. And the optimal difference set is selected for input of fuzzy predictor. The proposed method based the Takigi-Sugeno-Kang(TSK or TS) fuzzy rule. Computer simulations show improved results for various time series.

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신경회로망을 이용한 동적 시스템의 상태 공간 인식 모델에 관한 연구 (A Study on the State Space Identification Model of the Dynamic System using Neural Networks)

  • 이재현;강성인;이상배
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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    • pp.115-120
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    • 1997
  • System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

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A Controller Design for Teleoperated Systems with Signal Transmission Time Delay

  • Ahn, Sung-Ho;Jin, Jae-Hyun;Park, Byung-Suk;Yoon, Ji-Sup
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2002년도 ICCAS
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    • pp.116.1-116
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    • 2002
  • When the teleoperated system has a signal transmission time delay between slave and control system , the system stability as well as the position tracking and the force reflecting performances are likely to be deteriorated. This paper proposed a bilateral control scheme and a controller design method for the teleoperated control systems with a signal transmission time delay. The proposed controller is a modified type of smith predictor for the time delay in each input and output stage of an open loop unstable plant. The proposed controller not only satisfies the system internal stability but also improves the position tracking performance with disturbance rejection capability. The simulation...

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Position Control of Linear Actuator with Uncertain Time Delay in VDN

  • Kim, Jonghwi;Kiwon Song;Park, Gi-Sang;Park, Gi-Heung
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2002년도 ICCAS
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    • pp.118.2-118
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    • 2002
  • Uncertain time delay happens when the process reads the sensor data and sends the control input to the plant located at a remote site in distributed control system. As in the case of data network using TCP/IP, VDN that integrates both device network and data network has uncertain tim e delay. Uncertain time delay can cause degradation in stability of distributed control system based on VDN. This paper investigates the transmission characteristic of VDN and suggests a control scheme based on the Smith's predictor to minimize the effect of uncertain time delay. The validity of the proposed control scheme is demonstrated with tracking position control of experiments.

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퍼지 논리 제어기를 이용한 아크용접 공정제어 (Fuzzy linguistic control of arc welding process)

  • 부광석;양완행;조형석
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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    • pp.356-361
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    • 1990
  • This paper presents a new self organizing fuzzy linguistic control (SOFLC) strategy for application to an arc welding process control. The proposed SOFLC is based on on-line modification of the control rules according to the extent of deviation of the one step ahead predictive output of the process from the desired output. The Predictive output of the process is estimated by a fuzzy predictor which is updated from the input and output data of the process. The rule base of the fuzzy subsets describing the control rules is modified by the improving mechanism based on the hill climbing approach. Simulation results show that this proposed SOFLC improves the response of the process in presence of the variation of the process dynamic characteristics.

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지연시간을 갖는 프로세스를 위한 슬라이딩모드 가변구조 제어기 (Sliding Mode Controller for Process with Time Delay)

  • 김석진;박귀태;이기상;송명현;김성호
    • 대한전기학회논문지
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    • 제43권7호
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    • pp.1158-1168
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    • 1994
  • A variable structure control scheme(VSCS) with sliding mode that can be applied to the process with input/output(I/O) delay is proposed and its control performances is evaluated. The proposed VSCS with and output feedback scheme comprises a variable structure controller, a servo dynamic for tracking the set-poing, and a Smith predictor for compensating the effects of time delay. The robustness against the parameter variations and external disturbances can be achieved by the proposed VSCS even when the controlled process includes I/O delay. And the desired transient response is obtained by simple adjustment of the coefficients of the switching surface equation.

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불구속연쇄 동적시스템을 위한 최적설계 프로그램 개발 (Development of An Optimal Design Program for Open-Chain Dynamic Systems)

  • 최동훈;한창수;이동수;서문석
    • 대한기계학회논문집
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    • 제18권1호
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    • pp.12-23
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    • 1994
  • This paper proposes an optimal design software for the open-chain dynamic systems whose governing equations are expressed as differential equation. In this software, an input module and an automatic creation module of the equation of motion are developed to contrive the user's convenience. To analyze the equation of motion of the dynamic systems, variable-order and variable-stepsize Adams-Bashforth-Moulton predictor-corrector method is used to improve the efficiency. For the optimization and the design sensitivity analysis, ALM(augmented lagrange multiplier)method and adjoint variable method are adopted respectively. An output module with which the user can compare and investigate the analysis and the optimization results through tables and graphs is also provided. The developed software is applied to three typical dynamic response optimization problems, and the results compare very well with those available in the literature, demonstrating its effectiveness.

혼돈 비선형 시스템의 퍼지 신경 회로망 기반 일반형 예측 제어 (Fuzzy Neural Network Based Generalized Predictive Control of Chaotic Nonlinear Systems)

  • Park, Jong-Tae;Park, Yoon-Ho
    • 대한전기학회논문지:시스템및제어부문D
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    • 제53권2호
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    • pp.65-75
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    • 2004
  • This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

신경망 예측기를 이용한 인버티드 펜듈럼의 제어 (Control of an Inverted Pendulum Using Neural Network Predictor)

  • 문형석;이규열;강영호;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1996년도 하계학술대회 논문집 B
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    • pp.1031-1033
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    • 1996
  • Now is an automation age. Therefore it is required that machine can do work which was done by men. Artificial Neural Network was developed by the necessity of this purpose. This paper shows a Predictive Control with a Neural Network. The Neural Network learns an Inverted Pendulum in various situations. Then, it has a power to predict the next state after accept the current state. And the Neural Network directs the Bang-Bang Controller to give input to a plant. It seems like that a human expert looks the state of a plant and then controls the plant. It is used a Feedforward Neural Network and shown control state according to the learning. We could get a satisfactory results after complete learning.

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