• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.43-50
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• 2002

In the recent years neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neural network control for robot manipulators based on the radial basis function netwo.k (RBFN). The RBFN is a branch of the neural networks and is mathematically tractable. So we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. The saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.

• 말소리와 음성과학
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• 제11권2호
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• pp.57-64
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• 2019

본 논문에서는 음성 합성을 위한 오픈소스 시스템인 Merlin 툴킷을 이용하여 한국어 TTS 시스템을 구성한다. TTS 시스템에서 HMM 기반의 통계적 음성 합성 방식이 널리 사용되고 있는데, 이 방식에서 문맥 요인을 포함시키는 음향 모델링 구성의 한계로 합성 음성의 품질이 저하된다고 알려져 있다. 본 논문에서는 여러 분야에서 우수한 성능을 보여 주는 심층 신경망 기법을 적용하는 음향 모델링 아키텍처를 제안한다. 이 구조에는 전연결 심층 피드포워드 신경망, 순환 신경망, 게이트 순환 신경망, 단방향 장단기 기억 신경망, 양방향 장단기 기억 신경망 등이 포함되어 있다. 실험 결과, 문맥을 고려하는 시퀀스 모델을 아키텍처에 포함하는 것이 성능 개선에 유리하다는 것을 알 수 있고, 장단기 기억 신경망을 적용한 아키텍처가 가장 좋은 성능을 보여주었다. 그리고 음향 특징 파라미터에 델타와 델타-델타 성분을 포함하는 것이 성능 개선에 유리하다는 결과가 도출되었다.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2004년도 춘계학술발표논문집
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• pp.86-96
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• 2004

Identification of dynamics of short circulation during grade change operations in paper mills is very important for the effective plant operation. In the present study a prediction method of One Pass Retention(OPR) is proposed based on the neural network. The present method is used to analyze the dynamics of short circulation during grade change. Properties of the product paper largely depend upon the change in the OPR. In the present study the OPR is predicted from the training of the network by using grade change operation data. The results of the prediction are applied to the modeling equation to give flow rates and consistencies of short circulation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.198-203
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• 1995

In the paper, we have proposed a new technique to detemine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed totrain the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energyas well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of te neural network. The amount of incomplete filling in the die, load and forming energyas well as effective strain are measured by the rigid-plastic finite element method. The new technique is applied tofind the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determing the optimal billet of forging products, further it is usefully adapted to physical modeling for the forging design.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.261-264
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• 2004

In this paper, we identify the memory effect of high power(125W) laterally diffused metal oxide-semiconductor(LDMOS) RF Power Amplifier(PA) by two tone IMD measurement. We measure two tone IMD by changing the tone spacing and the power level. Different asymmetric IMD is founded at different center frequency measurements. We propose the Tapped Delay Line-Neural Network(TDNN) technique as the modeling method of LDMOS PA based on two tone IMD data. TDNN's modeling accuracy is highly reasonable compared to the memoryless adaptive modeling method.

• 전기학회논문지P
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• 제62권2호
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• pp.65-71
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• 2013

Nonlinear loads with harmonics exist in an actual power system where harmonic currents make voltage distortion. The sum of reactive power measured at individual load is different from the measured reactive power at a bus in a power system with linear and non-linear loads. In this study, ANN(artificial neural network) load modeling technique with consideration of harmonics is introduced for more accurate component load modeling and an impact coefficient is proposed for aggregation of component loads. Results of this research show more accurate load modeling method. Since precise data for power system analysis can be acquired, the proposed method will be used for power system planning and maintenance.

• 대한기계학회논문집
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• 제16권7호
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• pp.1322-1331
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• 1992

본 연구에서는 Fig.3과 같은 다층 퍼셉트론을 사용하기로 한다. 그리고 위 에서 언급한 세가지점에서 다층퍼셉트론을 다시 살펴보아 해결하고자 하는 문제에 맞 도록 다층퍼셉트론을 개선시켜 보기로 한다. 따라서 본 연구의 목적은 제한조건을 갖는 문제를 풀기위한 새로운 형태의 다층퍼셉트론 설계 및 이에 적합한 학습규칙을 적용하여 보다 간단한 구조와 빠른 학습시간을 갖는 신경망을 구성하는데 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1017-1021
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• 1991

This paper is to explore the potential use of the modeling capacity of neural networks for control applications. The tasks are carried out by two neural networks which act as a plant identifier and a system controller, respectively. Using information stored in the identification network control action has been developed. Without supervising control signals are generated by a gradient type iterative algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.139-142
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• 1996

This paper presents a method of controlling contact force for deburring tasks. The cope with the nonlinearities and time-varying properties of the robot and the environment, a neural network control theory is applied to design the contact force control system. We show that the contact force between the hand and the contacting surface can be controlled by adjusting the command velocity of a robot hand, which is accomplished by the modeling of a robot and the environment as Mass-Spring-Damper system. Simulation results are shown.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.218-223
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• 1992

This paper describes a learning method of neural network controllers. The learning method improves the performance of indirect learning mechanism in the neuro-control of nonlinear systems. To precisely identify dynamic characteristics of the plant by utilizing a limited prior information we propose a new energy function which takes advantage of the proportional relationship between outputs of the plant and those of neural networks.