• Journal of information and communication convergence engineering
• /
• 제13권2호
• /
• pp.123-131
• /
• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• 조명전기설비학회논문지
• /
• 제28권1호
• /
• pp.79-87
• /
• 2014

With the increase of demand for electricity power, new construction and expansion of transmission lines for transport have been required. However, it has been difficult to be realized by such opposition from environmental groups and residents. Therefore, the development of techniques for effective use of existing transmission lines is more needed. In this paper, the major variables to affect the allowable transmission capacity in an overhead transmission lines were selected and the dynamic line rating (DLR) method using artificial neural networks reflecting unique environment-heat properties was proposed. To prove the proposed method, the analyzed results using the artificial neural network were compared with the ones obtained from the existing method. The analyzed results using the proposed method showed an error of 0.9% within ${\pm}$, which was to be practicable.

• 한국통신학회논문지
• /
• 제22권2호
• /
• pp.259-267
• /
• 1997

The advent of B-ISDN using ATM(asynchronous transfer mode) made possible a variety of new multimedia services, however it also created a problem of congestion control due to bursty nature of various traffic sources. To tackle this problem, UPC/NPC(user parameter control/network parameter control) have been actively studied and DRLB(dynamic rate leaky bucket) algorithm, in which the token generation rate is changed according to states of data source andbuffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has drawbacks of low efficiency and difficult real-time implementation for bursty traffic sources because the determination of token generation rate in the algorithm is based on the present state of network. In this paper, we propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations.

• 융합신호처리학회논문지
• /
• 제13권4호
• /
• pp.233-237
• /
• 2012

본 논문에서는 RTDNN과 FLC를 이용해서 주신경망을 보상하는 제어시스템을 제안한다. 주신경망이 학습을 완료한 후 외란이나 부하변동이 생겨 오브 슛 내지는 언더 슛을 나타낼 때 적절히 조정하기 위해 퍼지 보상기를 사용하여 원하는 결과를 얻을 수 있도록 하였다. 그리고 제어대상의 역모델 신경망에서 학습시킨 결과를 이용하여 주신경망의 가중치를 변경시킴으로서 제어대상의 원하는 동적 특성을 얻게 된다. 모의 실험 결과 제안한 신경망 제어기의 양호한 응답 특성을 확인 할 수 있다.

• Journal of information and communication convergence engineering
• /
• 제7권2호
• /
• pp.125-130
• /
• 2009

A robust friction compensation scheme is proposed in this paper. The recurrent fuzzy neural network and friction parameter observer are developed with sliding mode based controller in order to obtain precise position tracking performance. For a servo system with incomplete identified friction parameters, a proposed control scheme provides a satisfactory result via some experiment.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.253-253
• /
• 2000

Each robot plays a role of its own behavior in dynamic robot-soccer environment. One of the most necessary conditions to win a game is control of robot movement. In this paper we suggest a win strategy using Cellular Neural Network to set optimal path and cooperative behavior, which divides a soccer ground into grid-cell based ground and has robots move a next grid-cell along the optimal path to approach the moving target.

• 한국정보통신학회논문지
• /
• 제24권6호
• /
• pp.809-817
• /
• 2020

소형위성 전력분배 및 전송모듈의 설계와 개발과정에서 딥러닝 알고리즘으로 동적 전력자원의 안정성을 평가하였다. 안정성 평가에 따른 요구사항은 소형위성 탑재체인 SAR 레이더의 전력분배모듈과 수요모듈의 전력전송기능을 구성하였다. 전력모듈인 PDM을 구성하는 스위칭 전력부품의 성능확인을 위해 동적신경망을 활용하여 신뢰성을 검증하였다. 신뢰성 검증을 위한 딥러닝 적용대상은 소형위성 본체로부터 공급되는 전력에 대한 탑재체의 전력분배기능이다. 이 기능에 대한 성능확인을 위한 모델링 대상은 출력전압변화추이(Slew Rate Control), 전압오류(Voltage Error), 부하특성(Load Power)이다. 이를 위해 첫째, 모델링으로 Coefficient Structure 영역을 정의하고 PCB모듈을 제작하여 안정성과 신뢰성을 비교 평가하였다. 둘째, 딥러닝 알고리즘으로 Levenberg-Marquare기반의 Two-Way NARX신경망 Sigmoid Transfer를 사용하였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.368-368
• /
• 2000

In this paper, we present forecasting ozone concentration with decision support system. Since the mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, modeling of ozone prediction system has many problems and results of prediction are not good performance so far. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
• /
• pp.227-233
• /
• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.

• 한국정밀공학회지
• /
• 제16권6호
• /
• pp.133-140
• /
• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.