• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.311-314
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제26권4호
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Steel and Composite Structures
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• 제44권5호
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• pp.707-720
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• 2022

In the present work, bending and free vibration analyses of multilayered functionally graded (FG) graphene platelet (GPL) and fiber-reinforced hybrid composite beams are carried out using the parabolic function based shear deformation theory. Parabolic variation of transverse shear stress across the thickness of beam and transverse shear stress-free conditions at top and bottom surfaces of the beam are considered, and the proposed formulation incorporates a transverse displacement field. The present theory works only with four unknowns and is computationally efficient. Hamilton's principle has been employed for deriving the governing equations. Analytical solutions are obtained for both the bending and free vibration problems in the present work considering different variations of GPLs and fibers distribution, namely, FG-X, FG-U, FG-Λ, and FG-O for beams having simply-supported boundary condition. First, the matrix is assumed to be strengthened using GPLs, and then the fibers are embedded. Multiscale modeling for material properties of functionally graded graphene platelet/fiber hybrid composites (FG-GPL/FHRC) is performed using Halpin-Tsai micromechanical model. The study reveals that the distributions of GPLs and fibers have significant impacts on the stresses, deflections, and natural frequencies of the beam. The number of layers and shape factors widely affect the behavior of FG-GPL-FHRC beams. The multilayered FG-GPL-FHRC beams turn out to be a good approximation to the FG beams without exhibiting the stress-channeling effects.

• International Journal of Aeronautical and Space Sciences
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• 제8권1호
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• pp.140-147
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• 2007

Whenever the hinge axis of aircraft wing rotates, its stiffness varies. Also, there are nonlinearities in the connection of the actuator and the hinge axis, and it is necessary to inspect the coupled effects between the actuator dynamics and the hinge nonlinearity. Nonlinear aeroelastic characteristics are investigated by using the iterative V-g method. Time domain analyses are also performed by using Karpel's minimum state approximation technique. The doublet hybrid method(DHM) is used to calculate the unsteady aerodynamic forces in subsonic regions. Structural nonlinearity located in the load links of the actuator is assumed to be friction. The friction nonlinearity of an actuator is identified by using the describing function technique. The nonlinear flutter analyses have shown that the flutter characteristics significantly depends on the structural nonlinearity as well as the dynamic stiffness of an actuator. Therefore, the dynamic stiffness of an actuator as well as the nonlinear effect of hinge axis are important factors to determine the flutter stability.

• 한국항공우주학회지
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• 제33권2호
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• pp.54-59
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• 2005

구동기의 백래쉬와 동강성을 고려한 미사일 조종날개의 비선형 공탄성 해석이 수행되었다. 아음속 비정상 공기력 계산을 위해 DHM을 사용하였고 최소상태접근법을 사용하여 근사하였다. 비선형 플러터 해석을 위해 백래쉬는 유격으로 모델하고 기술 함수법을 사용하여 선형화하였다. 또한, 동강성은 주파수의 함수로 모터의 운동방정식으로부터 계산하였다. 선형 및 비선형 플러터 해석 결과들은 공력탄성학적 특성들이 백래쉬와 동강성에 중요한 영향을 받는다는 것을 보여준다. 비선형 플러터 해석에서 다양한 제한 주기 운동이 선형플러터 속도 이하에서 관측되었다. 또한 플러터 특성과 응답을 시간영역에서도 조사하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.