• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1021-1024
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• 1999

In this paper, we propose a modified feedback neural network structure for adaptive control of robot manipulators. The proposed structure is that all of network output feedback into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the new neural network structure in the adaptive control of robot manipulators.

• Smart Structures and Systems
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• 제19권4호
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• pp.383-391
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• 2017

In this paper, we investigate the impacts of network topology on the performance of a distributed estimation algorithm, namely combine-then-adaptive (CTA) diffusion LMS, based on the data with or without the assumptions of temporal and spatial independence with noisy links. The study covers different network models, including the regular, small-world, random and scale-free whose the performance is analyzed according to the mean stability, mean-square errors, communication cost (link density) and robustness. Simulation results show that the noisy links do not cause divergence in the networks. Also, among the networks, the scale free network (heterogeneous) has the best performance in the steady state of the mean square deviation (MSD) while the regular is the worst case. The robustness of the networks against the issues like node failure and noisier node conditions is discussed as well as providing some guidelines on the design of a network in real condition such that the qualities of estimations are optimized.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.456-459
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• 2001

본 논문에서는 웨이브렛 신경회로망을 이용한 적응 제어 방식을 제안한다. 웨이브렛 신경망의 구조는 은닉층의 시그모이드 함수를 mother 웨이브렛 함수로 대치한 것을 제외하고는 다층 신경회로망 구조와 비슷하다. 단일 관절 매니률레이터를 대상으로 적응 제어 시뮬레이션을 수행한 결과 웨이브렛 신경회로망의 우수성을 확인하였다.

• ETRI Journal
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• 제27권6호
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• pp.802-805
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• 2005

A dynamic bandwidth allocation (DBA) scheme, an inter -optical network unit (ONU) bandwidth scheduling, is presented to provide quality of service (QoS) to different classes of packets in Ethernet passive optical networks (EPONs). This scheme, referred to as TADBA, is based on efficient threshold reporting from, and adaptive polling order rearranging of, ONUs. It has been shown that the network resources are efficiently allocated among the three traffic classes by guaranteeing the requested QoS, adaptively rearranging the polling orders, and avoiding nearly all fragmentation losses. Simulation results using an OPNET network simulator show that TADBA performs well in comparison to the available allocation scheme for the given parameters, such as packet delay and channel utilization.

• International Journal of Control, Automation, and Systems
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• 제3권1호
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• 음성과학
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• 제11권4호
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• pp.89-97
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• 2004

In this paper, an improved network echo canceller is proposed. The proposed echo canceller is based on the LTJ(lattice transversal joint) adaptive filter which uses informations from the speech decoder. In the proposed implementation method of the network echo canceller, the filer coefficients of the transversal filter part in the LTJ adaptive filter is updated every other sample instead of every sample. So its complexity can be lower than that of the transversal filter. And the echo cancellation rate can be improved by residual echo cancellation using the lattice predictor whose order is less than 10. Computational complexity of the proposed echo canceller is lower than that of the transversal filter but the convergence speed is faster than that of the transversal filter. The performance improvement of the proposed echo canceller was verified by the experiments using the real speech signal and speech coder.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.285-290
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• 2006

This paper presents an adaptive neural network based controller and its application to Dynamic Positioning (DP) control system of ship. The proposed neural network based controller is developed for station-keeping and low-speed maneuvering control of ship. At first, the DP system configuration is described. And then, to validate the proposed DP system, computer simulations of station-keeping and low-speed maneuvering performance of a multi-purpose supply ship are presented under the influence of measurement noise, external disturbances such as sea current, wave, and wind. The simulations have shown the feasibility of the DP system in various maneuvering situations.

• 대한전기학회논문지
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• 제41권8호
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• pp.914-921
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• 1992

In this paper, we introduce a Walsh network and an LMS algorithm, and show how these can be realized as an adaptive equalizer. The Walsh network is built from a set of Walsh and Block pulse functions. In the LMS algorithm, the convergence factor is an important design parameter because it governs stability and convergence speed, which depend on the proper choice of the convergence facotr. The conventional adaptation techniques use a fixed time constant convergence factor by the method of trial and error. In this paper, we propose an optimal method in the choice of the convergence factor. The proposed algorithm depends on the received signal and the output of the Walsh network in real time.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.446-449
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• 1997

We propose a control law in discrete time domain of the bilateral feedback teleoperation system using neural network and the reference model type of adaptive control. Different from traditional teleoperation systems, the transmission time delay irregularly changes. The proposed control method controls master and slave systems through identification of master and slave models using neural networks.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the 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. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.