• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1994년도 추계학술대회 논문집
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• pp.133-140
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• 1994

In this paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in neural networks. the architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of neural network, an optimal billet is determined by applying nonlinear mathematical relationship between shape ratio in the initial billet and the final products. A volume of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet shape ratio and that of the un-filled volume. After learning, the system is able to predict the filling region which are exactly the same or slightly different to results of finite element method. It is found that the prediction of the filling shape ratio region can be made successfully and the finite element method results are represented better by the neural network.

• 제어로봇시스템학회논문지
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• 제14권11호
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• pp.1103-1109
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• 2008

Since the flexible joint robots with motor dynamics are represented by the fifth-order nonlinear sγstem, it is difficult and complex to design the controller for electrically driven flexible-joint (EDFJ) robots. In this paper, we propose a simple adaptive control method to solve this problem. It is assumed that the model uncertainties of the robots dynamics, joint flexibility, and motor dynamics are unknown. For the simple control design, the dynamic surface design method is applied, and all uncertainties in the robot and motor dynamics are compensated by using the adaptive function approximation technique. It is proved that all signals in the controlled closed-loop system are uniformly ultimately bounded. Simulation results for three-link EDFJ manipulators are provided to validate the effectiveness of the proposed control system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.199-203
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• 1997

In this paper is proposed a new direct adaptive fuzzy controller that dan ve applied for tracking control of a class of uncertain nonlinear SISO systems. It is shown that, in the presence of the perturbations such as fuzzy approximation error and external disturbance, boundedness of all the signals in the system is ensured, while under the assumption of no perturbations, the stability of the overall system in guaranteed. Also, the concept of persistent excitation in the adaptive fuzzy control systems is introduced to guarantee the convergence and the boundedness of adaptation parameter in the proposed controllers. Simulation example shows the effectiveness of the proposed method in the presence of fuzzy approximation error and external disturbance.

• 한국지능시스템학회논문지
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• 제6권3호
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• pp.25-36
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• 1996

기존의 RBF 신경망 및 퍼지 시스템을 고차원 입력 공간 위에서의 비선형 근사에 적용할 경우 은닉 노드의 수혹은 퍼지 IF-THEN 규칙의 수가 기하급수적으로 증가한다. 본 논문에서는 이러한 문제점을 개선하기 위해 반국소 유닛을 기본 요소로 하는 수목구조지능시스템을 제안하고, 이를 효과적으로 학습하기 위하여 수정 유전자 알고리즘 및 LMS 규칙에 기반을 둔 학습 알고리즘을 개발한다. 제안된 시스템에 대한 근사 능력 해석이 수행되고, 실험적 고찰을 통하여 개발된 방법론의 유용성이 입증된다.

• International Journal of Control, Automation, and Systems
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• 제1권2호
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• pp.171-177
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• 2003

In this paper, we investigate a robust $H_{\infty}$ state feedback control technique for continuous time bilinear systems with an additive disturbance input. The nonlinear robust $H_{\infty}$control for bilinear systems requires a solution to the state dependent algebraic Riccati equation (SDARE). We present a new robust $H_{\infty}$control technique based on the successive approximation method for solving the SDARE by converting bilinear systems into time-varying linear systems. The proposed control method guarantees robust stability for closed loop bilinear systems. The proposed algorithm is verified by numerical examples.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1505-1506
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• 2008

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control(EFC) using a sliding mode adaptive flux observer and robust speed control(RSC) using a function approximation for mechanical uncertainties. In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer(NDO) using fuzzy neural networks(FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.695-700
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• 2008

Artificial neural network (ANN) has been extensively used in areas of nonlinear system modeling, analysis and design applications. Basically, ANN has its distinct capabilities of implementing system identification and/or function approximation using a number of input/output patterns that can be obtained via numerical and/or experimental manners. The paper describes a role of ANN, especially a back-propagation neural network (BPN) in the context of engineering analysis, design and optimization. Fundamental mechanism of BPN is briefly summarized in terms of training procedure and function approximation. The BPN based causality analysis (CA) is further discussed to realize the problem decomposition in the context of multidisciplinary design optimization. Such CA is also applied to quantitatively evaluate the uncoupled or decoupled design matrix in the context of axiomatic design with the independence axiom.

• 유공압시스템학회논문집
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• 제1권1호
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• pp.1-9
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• 2004

This paper addresses an approximation and tracking control of recurrent neural networks(RNN) using two-dimensional iterative learning algorithm for an electro-hydraulic servo system. And two dimensional learning rule is driven in the discrete system which consists of nonlinear output function and linear input. In order to control the trajectory of position, two RNN's with the same network architecture were used. Simulation results show that two RNN's using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RNN was very effective to control trajectory tracking of electro-hydraulic servo system.

• 말소리와 음성과학
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• 제2권1호
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• pp.87-95
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• 2010

In this paper, we address issues associated with mixed-lingual text-to-speech based on context-dependent HMMs, where there are multiple sets of HMMs corresponding to each individual language. In particular, we propose smoothing techniques of synthesis parameters at the boundaries between different languages to obtain more natural quality of speech. In other words, mel-frequency cepstral coefficients (MFCCs) at the language boundaries are smoothed by applying several linear and nonlinear approximation techniques. It is shown from an informal listening test that synthesized speech smoothed by a modified version of linear least square approximation (MLLSA) and a quadratic interpolation (QI) method is preferred than that without using any smoothing technique.

• International Journal of Aeronautical and Space Sciences
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• 제7권1호
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• pp.65-72
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• 2006

A variable structure controller with an optimized sliding surface is proposed for slew maneuver of a rigid spacecraft. Rodrigues parameters are chosen to represent the spacecraft attitude. The quadratic type of performance index is used to design the sling surface. For optimization of the sliding surface, a Hamilton- Jacobi-Bellman equation is formulated and it is solved through the numerical algorithm using Galerkin approximation. The solution denotes a nonlinear sliding surface, on which the trajectory of the system satisfies the optimality condition approximately. Simulation result demonstrates that the proposed controller is effectively applied to the slew maneuver of a rigid spacecraft.