• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.969-971
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• 1999

Wavelet analysis is applying to many fields such as the time-frequency localization of a time series and a time varying data. In this paper, a statistical testing based Wavelet power spectrum analysis for the stationary Nino3 Sea Surface Temperature(SST) data was executed. Specially, the 95% confidence level for SST was effective in searching the periods of El-Nino using various wavelet basis functions.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.511-516
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• 2002

신경회로망은 차원이 확장됨에 따라 학습에 필요한 계산량이 기학급수적으로 증가하는 문제가 발생한다. 이를 극복하기 위해 직교성을 지닌 웨이블릿 신경회로망이 제안되었다. 웨이블릿 함수의 경우 스케일과 중심을 결정함으로써 신경회로망의 노드로 구성된다. 본 논문에서는 웨이블릿 함수를 이용하여 망을 구성하는 과정에 스케일링 함수를 함께 은닉층의 노드로 복합 구성함으로써 스케일링 함수를 이용하여 대강 근사(rough approximation)를 행한 다음, 웨이블릿 함수를 이용하여 미세 근사(fine approximation)를 행하도록 구성하는 복합 신경회로망을 제안한다. 또한, 복합 신경회로망을 구성하는 과정에서 미세 근사에 필요한 웨이블릿 함수의 개수를 유전 알고리즘을 이용하여 결정한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.723-725
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• 2004

An iterative robust control design for PZT using Gaussian wavelet networks is proposed. A Gaussian wavelet network with accurate approximation capability is employed to approximate the nonlinear hysteresis dynamics of PZT systems by using an iterative control algorithm. Depending on the finite number of wavelet basis functions which results in unavoidable approximation errors, a robust control law is provided to guarantee the stability of the closed-loop nano positioning system. Finally, the effectiveness of the robust control approach is illustrated through comparative simulations on a PZT.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제12권4호
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• pp.237-251
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• 2005

In this paper, a new wavelet analysis of differential operator spline is generated, and it is of the symmetry and (3 -$\epsilon$ )-order regula.ity (0 < $\epsilon$ < 3). Finally, using this wavelet basis, we expand Lebesgue square integrable functions efficiently and quickly.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Structural Engineering and Mechanics
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• 제25권5호
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• pp.613-629
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• 2007

A finite element method (FEM) of B-spline wavelet on the interval (BSWI) is used in this paper to solve the static and vibration problems of thin plate. Instead of traditional polynomial interpolation, the scaling functions of two-dimensional tensor product BSWI are employed to construct the transverse displacements field. The method combines the accuracy of B-spline functions approximation and various basis functions for structural analysis. Some numerical examples are studied to demonstrate the proposed method and the numerical results presented are in good agreement with the solutions of other methods.

• 한국정보통신학회논문지
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• 제9권3호
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• pp.467-472
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• 2005

많은 임상적 상태에서 ECG신호는 진단을 목적으로 기록된다. 또한 정확한 임상해석을 위해 데이터는 높은 해상도와 샘플링율이 필요하다. 따라서 본 논문에서는 다중웨이브렛 기저함수를 이용한 심전도 압축구조를 설계하여 기존의 단일 웨이브렛 기저함수와 이산 코사인 변환과 비교 분석하였다. 실험의 객관성을 위해 MIT-BIH 데이터 베이스중에서 분해도가 11[bit]이고 샘플링 주파수가 360[Hz]인 부정맥 데이터를 이용하여 모의 실험하였다. 성능평가는 재생오차에 대한 압축율로 평가하였다. 결과적으로 다중웨이브렛 기저함수를 이용한 심전도 압축구조에서 DCT보다 2배 이상의 좋은 성능평가 결과를 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2432-2434
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.467-476
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• 2004

Recently, an approximation of a wavelet series has been developed in the analyses of an unknown function. Most of articles have been studied on thresholding and shrinkage methods for its wavelet coefficients based on (non)parametric and Bayesian methods when the sample size is considered as a maximum resolution in wavelet series. In this paper, regardless of the sample size, we are focusing only on the choice of a maximum resolution in wavelet series. We propose a Bayesian approach to the choice of a maximum resolution based on the linear combination of the wavelet basis functions.