• Title/Summary/Keyword: nonlinear system modeling

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Bilinear Modeling of Grade Change Operation in Paper Mills (지종교체 공정의 Bilinear 모델링)

  • Chu, Yeon-Uk;Yeo, Yeong-Gu;Gang, Hong
    • Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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    • 2004.04a
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    • pp.97-106
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    • 2004
  • The paper making process itself is a typical nonlinear process with complicated dynamics. In the application of advanced control-methods especially for the grade change operations the nonlinear process is linearized to give suitable linear models to be used in the control strategies. However, the use of the linear model is limited within short range containing steady-state operating conditions for grade change operation. In this paper a bilinear model for the nonlinear grade change processes is presented. We can see that the dynamic behavior for grade change operations can be effective analyzed by using multivariable bilinear model.

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Performance Improvement of Nonlinear System Modeling Using GMDH (GMDH를 이용한 비선형 시스템의 모델링 성능 개선)

  • Hong, Yeon-Chan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.7
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    • pp.1544-1550
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    • 2010
  • There have been many researches applying GMDH for modelling nonlinear dynamic systems. However, these methods require a great amount of computation in return of the accuracy. Thus, in this paper, we propose a method to reduce the amount of computation in GMDH by adjusting the adopting criterion of input data in decrement while at least maintaining the accuracy. The simulation result verifies that the proposed method can successfully reduce the amount of computation without the expense of the error rate, if not significantly better.

Fault Diagnosis of Nonlinear Systems Based on Dynamic Threshold Using Neural Network (신경회로망을 이용한 동적 문턱값에 의한 비선형 시스템의 고장진단)

  • Soh, Byung-Seok;Lee, In-Soo;Jeon, Gi-Joon
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.11
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    • pp.968-973
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    • 2000
  • Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

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Parallel Structure Modeling of Nonlinear Process Using Clustering Method (클러스터링 기법을 이용한 비선형 공정의 병렬구조 모델링)

  • 박춘성;최재호;오성권;안태천
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.383-386
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    • 1997
  • In this paper, We proposed a parallel structure of the Neural Network model to nonlinear complex system. Neural Network was used as basic model which has learning ability and high tolerence level. This paper, we used Neural Network which has BP(Error Back Propagation Algorithm) model. But it sometimes has difficulty to append characteristic of input data to nonlinear system. So that, I used HCM(hard c-Means) method of clustering technique to append property of input data. Clustering Algorithms are used extensively not only to organized categorize data, but are also useful for data compression and model construction. Gas furance, a sewage treatment process are used to evaluate the performance of the proposed model and then obtained higher accuracy than other previous medels.

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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.

Voltage Control of PWM Converter Using Nonlinear Control (비선형 제어기법을 이용한 PWM 컨버터의 전압제어)

  • Lee, Ki-Do;Lee, Dong-Choon
    • Proceedings of the KIEE Conference
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    • 1997.11a
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    • pp.463-465
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    • 1997
  • For fast response of the dc output voltage in PWM converter, the relationship of power balance of both the input and output should be introduced to the system modeling. Then, a nonlinear control theory using state feedback linearization is useful to control the system. By nonlinear control, the voltage response can be fast, so the size of the output filter capacitor can be reduced as long as the same response is kept. The validity of the proposed scheme is shown from the simulation results.

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Erection Simulation Considering Interaction between a Floating Crane and a Heavy Cargo (해상크레인과 대형 중량물의 상호 작용을 고려한 탑재 시뮬레이션)

  • Cha, Ju-Hwan;Lee, Kyu-Yeul
    • Korean Journal of Computational Design and Engineering
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    • v.15 no.1
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    • pp.70-83
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    • 2010
  • Recently, floating cranes are mainly used to erect heavy blocks or cargos for constructing ships in many shipyards. It is important to estimate the dynamic motion of the heavy cargo suspended by a floating crane and the tension of the wire ropes between the floating crane and the heavy cargo. In this paper, the coupled dynamic equations of motion are set up for considering the 6 degree-of-freedom floating crane and the 6-degrees-of-freedom heavy cargo based on multibody system dynamics. Depending on the cargo weight, the motion of the floating crane would be changed to nonlinear state. The nonlinear terms in the equation of motion are considered. In addition, the nonlinear hydrostatic force, the linear hydrodynamic force, wire rope force, mooring force and gravity force are considered as the external forces. As the result of this paper, we analyze the engineering effect for erecting the heavy cargo by using the floating crane.

Dynamic Characteristics Modeling for A MR Damper using Artifical Neural Network (인공신경망을 이용한 MR댐퍼의 동특성 모델링)

  • 백운경;이종석;손정현
    • Transactions of the Korean Society of Automotive Engineers
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    • v.12 no.3
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    • pp.170-176
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    • 2004
  • MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

Improvement of line Current using Instantaneous Real Power Compensation of DSTATCOM (DSTATCOM의 순시 유효전력 보상을 이용한 선로의 전류 개선)

  • Jeong, Su-Yeong;Kim, Tae-Hyeon;Mun, Seung-Il;Gwon, Uk-Hyeon
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.51 no.7
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    • pp.327-332
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    • 2002
  • In this paper, conventional reactive power compensation is defined and instantaneous real control concept for shunt converters is proposed. This equipment incorporates the compensation function of harmonics at the distribution line by nonlinear load. These methodologies are applied to IEEE 13 distribution system with the modeling of nonlinear load using EMTEDC/PSCAD package. Simulation with EMTDC results presented to confirm that the new approach has better performance than those obtained by controllers based on traditional concepts of reactive power compensation.

Fuzzy Modeling Using Wavelet Transform and Genetic Algorithm (웨이브렛 변환과 유전 알고리듬을 이용한 퍼지 모델링)

  • Lee, Seung-Jun;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2327-2329
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    • 2000
  • This paper addresses the use of a nonlinear modeling procedure which construct a wavelet-based fuzzy model using genetic algorithm. A fuzzy inference system has the functional equivalence with a wavelet transform. Therefore, a wavelet-based fuzzy model using GA inherits the advantage of wavelet transform. Hereby, its performance is promoted. By help of the ability of GA to search the optimum globally, parameters of wavelet transform is determined closely to the optimal point. The feasibility of the proposed fuzzy model is proved by modelling a highly nonlinear function and comparing it with previous research.

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