• 호남수학학술지
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• 제30권2호
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• pp.369-378
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• 2008

Modeling nonlinear friction effects is a challenging problem. In this paper, a tracking controller is proposed for a system with uncertain nonlinear friction dynamics. Instead of using a specific friction model, we assume that the friction dynamics are represented by a function, which is unknown except its being continuously differentiable and Lipschitz continuous with known Lipschitz constants. It is shown that the scheme results in friction identification and trajectory position and velocity tracking. The analysis is done using Lyapunov-based stability method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.827-829
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• 1999

The identification of nonlinear system using Extended GMDH(EGMDH) is studied in this paper. The proposed EGMDH algorithm is based on GMDH(Group Method of Data handling) method and its structure is similar to Neural Networks. The each node of EGMDH structure utilizes several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. As the operating condition changes, the parameters of EGMDH will also change, so the proposed scheme by means of the EGMDH method is capable of adapting rapidly to the changing environment. The simulation result shows that the simple nonlinear process can be modeled reasonably well by the proposed method which are simple but efficient.

• 한국지능시스템학회논문지
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• 제7권3호
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• pp.65-71
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• 1997

신경 회로망의 출현으로 비선형 시스템 모델링에 대한 관힘이 다시 고조되고 있다. 따라서 본 논문에서는 미지의 비선형 시스템을 동적으로 인식하기 위해 GMDH(Group Method of Data Handling) 일고리즘을 사용한 DPNN(Dynamic Polynomial Neural Network)을 제안한다. GMDH를 사용한 동적 시스템의 인신은 일렬의 입/출력 데이타를 인가하여 필요한 계수들의 집합을 동적으로 산출함으로써 훈련시킨다. 또한 DPNN을 이용하여 비선형 시스템을 제어하기 위해, MRA(Model Reference Adaptive Control)를 설계한다. 결과에서 컴퓨터 시뮬레이션을 통해 DPNN을 사용한 모델링과 제어가 잘 수행됨을 알 수 있었다.

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.50-55
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• 1998

GMDH(Group Method of Data Handing)는 복잡한 비선형 시스템을 인식하는데 유용한 데이타 분석 기법이다. 따라서 본 논문에서는 비선형 다이내믹 시스템을 모델링하기 위한 GMDH 알고리즘의 적용 방법을 제안한다. GMDH를 사용한 다이내믹 시스템의 인식은 일련의 입출력 데이타를 인가하여 필요한 계수들의 집합을 동적으로 산출함으로써 이루어진다. 또한, 본 논문에서는 데이타를 취사 선택하는 기준을 순차적으로 감소시킴으로써 GMDH의 단점인 계산량의 과다를 방지하는 방법도 제안하였다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.95-98
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• 1997

In this paper, several nonlinear system identification theories and the application of these methods to the physiological system are reviewed by extracting significant results from the literature. Methods based on unctional series expansion, parameter estimation, block-oriented models are included. However, there is still considerable debate about the advantages and disadvantages of each approach. This is true primarily because each method has limitations on the types of assumption and interpretation, types of nonlinear elements, etc. This means that user must select an appropriate method and the selection will depend on the problem under investigation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Journal of electromagnetic engineering and science
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• 제8권1호
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• pp.17-22
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• 2008

It is presented that sampling rates for behavioral modeling of quasi-memory less nonlinear devices can be far less than the Nyquist rate of the input signal. Although it has been believed that the sampling rate of nonlinear device modeling should be at least the Nyquist rate of the output signal, this paper suggests that far less than the Nyquist rate of the input signal can be applied to the modeling of quasi-memoryless nonlinear devices, such as frequency multipliers. To verify, a QPSK signal at 820 MHz were applied to a frequency tripler, whereby the device can be utilized as an up-converting mixer into 2.46 GHz with the aid of digital predistortion. AM-AM, AM-PM and PM-PM can be successfully measured regardless of sampling rates.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.127-131
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• 1993

A new learning method of both NNI and NNC by which the NNI identifies precisely the dynamic characteristics of the plant is proposed. For control of ihe nonlinear plant we use two neural networks, one -for identification and the other for control. We define a closed loop en-or which depends on identification and control error. In the proposed learning method, the closed loop en-or is utilized to train the NNI and the NNC. Computer simulation results reveal that the NNC based on proposed method is insensitive to variations of the plant parameters.

• 한국정보통신학회논문지
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• 제3권4호
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• pp.895-905
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• 1999

This paper identifies a linear time-invariant mathematical model of each stand of a five-stand tandem cold mill to design a robust $H_\infty$ thickness controller by applying input and output data sets to N4SID (Numerical algorithms for Subspace State Space System Identification) method. The input-output data sets describe interstand interference in the process of tandem cold rolling and are obtained from a nonlinear simulator of the tandem cold mill. In result, it is shown that the identified model well approximates the nonlinear model than a Taylor linearized model. Furthermore, uncertainties including roll eccentricity and incoming strip variation are quantitatively analyzed from the plot of maximum singular values.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.