• 한국정밀공학회지
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• 제15권4호
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• 음성과학
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• 제11권2호
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• pp.237-245
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• 2004

In general, Gaussian mixture model(GMM) is used to estimate the speaker model for speaker identification. The parameter estimates of the GMM are obtained by using the expectation-maximization (EM) algorithm for the maximum likelihood(ML) estimation. However, if the number of mixtures isn't defined well in the GMM, those parameters are obtained inappropriately. The problem to find the number of components is significant to estimate the optimal parameter in mixture model. In this paper, to estimate the optimal number of mixtures, we propose the method that starts from the sufficient mixtures, after, the number is reduced by investigating the mutual information between mixtures for GMM. In result, we can estimate the optimal number of mixtures. The effectiveness of the proposed method is shown by the experiment using artificial data. Also, we performed the speaker identification applying the proposed method comparing with other approaches.

• Smart Structures and Systems
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• 제4권4호
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• pp.465-492
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• 2008

Proper pretest planning is a vital component of any successful vibration test on engineering structures. The most important issue in dynamic testing of many engineering structures is arriving at the number and optimal placement of sensors. The sensors must be placed on the structure in such a way that all the important dynamic behaviour of a structural system is captured during the course of the test with sufficient accuracy so that the information can be effectively utilised for structural parameter identification or health monitoring. Several optimal sensor placement (OSP) techniques are proposed in the literature and each of these methods have been evaluated with respect to a specific problem encountered in various engineering disciplines like aerospace, civil, mechanical engineering, etc. In the present work, we propose to perform a detailed characteristic evaluation of some selective popular OSP techniques with respect to their application to practical civil engineering problems. Numerical experiments carried out in the paper on various practical civil engineering structures indicate that effective independence (EFI) method is more consistent when compared to all other sensor placement techniques.

• 한국지능시스템학회논문지
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• 제9권5호
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• pp.555-565
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• 1999

복잡하고 비선형적인 시스템의 규칙베이스 퍼지모델링을 위하여 퍼지시스템의 최적 동정알고리즘을 연구한다. 비선형 시스템은 퍼지모델의 입력변수와 퍼지 입력공간 분할에 의한 구조동정과 파라미터 동정을 통해 표현된다. 본 논문에서 규칙베이스 퍼지모델링은 비선형 시스템을 위해 퍼지추론방법과 두 종류의 최적화 이론의 결합에 의한 하이브리드 구졸를 이용하여 시스템 구조와 파라미터동정을 수행한다. 퍼지모델의 추론방법은 간략추론 및 선형추론에 의한다. 제안된 하이브리드 최적 동정 알고리즘은 유전자 알고리즘과 개선된 콤플렉스 방법을 이용한다. 여기서 유전자 알고리즘은 전반부 퍼지규칙의 멤버쉽함수의 초기 파라미터들을 결정하기 위해 사용되고 강력한 자동동조 알고리즘인 개선된 콤플렉스 방법은 정교한 파라미터들을 얻기 위해 수행된다. 따라서 최적 퍼지모델을 위해 전반부 파라미터 동정에는 하이브리드형의 최적 알고리즘을 이용하고 후반부 동정에는 최소자승법을 이용한다. 또한 학습과 테스트 데이터에 의해 생성된 퍼지모델의 성능결과 사이의 상호균형을 얻기 위해 하중계수를 가지는 합성 성능지수를 제안한다. 제안된 모델의 성능평가를 위해 두가지 수치적 예를이용한다.

• 대한수학회지
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• 제43권5호
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• pp.931-950
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• 2006

We study the identification problems of constant parameters appearing in the perturbed sine-Gordon equation with the Neumann boundary condition. The existence of optimal parameters is proved, and necessary conditions are established for several types of observations by utilizing quadratic optimal control theory due to Lions [13].

• 한국항해항만학회지
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• 제28권5호
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• pp.435-441
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• 2004

본 논문은 비선형성을 많이 내포하고 있어 수학적으로 모델링 하기 어려운 선박용 안정화 위성 안테나 시스템을 모델링하기 위해서, 신경 회로망의 오차 및 응답시간을 최소로 하는 최적 구조 신경 회로망 모델을 도출하고 이를 적용하고자 한다. 오차와 응답시간을 최소화하기 위해 유전알고리즘을 이용하여 신경 회로망 구조를 설계하였다. 안테나 시스템으로부터 얻어진 입출력 데이터에 거하여 본 논문에서 제안한 식별기를 이용하여 안테나 시스템을 식별하였으며, 실제 선박의 운동 성분에 대해서도 시스템을 잘 표현할 수 있는 최적 구조 신경 회로 기반 시스템 식별기를 얻을 수 있었다. 실제 실험을 통해서, 최적 신경회로망 구조가 안테나 시스템 식별에 효과적인 것을 알 수 있었다.

• 경영과학
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• 제17권2호
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• pp.1-12
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• 2000

In this study, we deals with the implementation of an optimal basis identification procedure for interior point methods. Our implementation is based on Megiddo’s strongly polynomial algorithm applied to Andersen and Ye’s approximate LP construction. Several techniques are explained such as the use of effective indicator for obtaining optimal partition when constructing the approximate LP, the efficient implementation of the problem reduction technique proposed by Andersen, the crashing procedure needed for fast dual phase of Megiddo’s algorithm and the construction of the stable initial basis. By experimental comparison, we show that our implementation is superior to the crossover scheme implementation.

• The Journal of the Acoustical Society of Korea
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• 제28권3E호
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• pp.109-117
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• 2009

Identity recognition in real environment with a reliable mode is a key issue in human computer interaction (HCI). In this paper, we present a robust person identification system considering score-based optimal reliability measure of audio-visual modalities. We propose an extension of the modified reliability function by introducing optimizing parameters for both of audio and visual modalities. For degradation of visual signals, we have applied JPEG compression to test images. In addition, for creating mismatch in between enrollment and test session, acoustic Babble noises and artificial illumination have been added to test audio and visual signals, respectively. Local PCA has been used on both modalities to reduce the dimension of feature vector. We have applied a swarm intelligence algorithm, i.e., particle swarm optimization for optimizing the modified convection function's optimizing parameters. The overall person identification experiments are performed using VidTimit DB. Experimental results show that our proposed optimal reliability measures have effectively enhanced the identification accuracy of 7.73% and 8.18% at different illumination direction to visual signal and consequent Babble noises to audio signal, respectively, in comparison with the best classifier system in the fusion system and maintained the modality reliability statistics in terms of its performance; it thus verified the consistency of the proposed extension.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권7호
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.