• Journal of applied mathematics & informatics
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• 제12권1_2호
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• pp.395-408
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• 2003

Common Channel Signaling(CCS) networks need special controls to avoid overload of signaling networks and degradation of call process-ing rate, since they play an important role of controlling communication transfer networks. Congestion control and flow control mechanisms are well described in ITU-T recommendation on Signaling System No.7(SS7). For the practical provisions, however, we need an analysis on the relation among service objects, system requirements and implementation of congestion control algorithms. SS7 provides several options for controlling link congestion in CCS networks. In this paper we give a general queueing model of congestion control algorithm which covers both the international and national options. From the queuing model, we obtain the performance parameters such as throughput, message loss rate and mean delay for the international option. To show the performance of the algorithm, some numerical results are also given.

• 제어로봇시스템학회논문지
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• 제5권8호
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• pp.932-940
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• 1999

In this paper, a new neural networks and neural network based sliding mode controller are proposed. The new neural networks are an mor self-recurrent neural networks which use a recursive least squares method for the fast on-line leammg. The error self-recurrent neural networks converge considerably last than the back-prollagation algorithm and have advantage oi bemg less affected by the poor initial weights and learning rate. The controller for suspension system is designed according to sliding mode technique based on new proposed neural networks. In order to adapt shding mode control mnethod, each frame dstance hetween ground and vehcle body is estimated md controller is designed according to estimated neural model. The neural networks based sliding mode controller approves good peiformance throllgh computer sirnulations.

• 전자공학회논문지SC
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• 제39권2호
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• pp.157-165
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• 2002

본 논문에서는 PD 제어기와 신경회로망을 이용하여 3상 유도전동기의 속도제어 시스템을 구현하고자 한다. PD 제어기는 초기의 제어를 담당하며 신경회로망의 초기 학습을 담당한다. 또한, 신경회로망은 비선형 매핑능력과 학습능력이 탁월하기 때문에 제어기로 많이 사용되며 특히 전향경로 신경망은 구조가 매우 간단하기 때문에 본 논문에서는 이를 이용하여 유도전동기의 속도제어 시스템에 구현하였다. 신경회로망의 입력으로는 모터의 기준속도, 엔코더를 이용하여 측정한 모터의 실제 속도와 제어입력 전류를 이용하였고, 온라인 상태로 학습되도록 하였다. 본 논문에서 제안된 알고리즘의 타당성을 보이기 위해 기존에 널리 사용되었던 PI 제어기와 비교평가를 하였으며 시뮬레이션과 실험결과로부터 초기운전 상태에서는 PD 제어기가 주로 제어를 담당하지만 시간이 지남에 따라 신경회로망이 학습되어 신경회로망이 주 제어기가 됨을 확인하였다. 아울러, 제안된 하이브리드 제어기가 PI 제어기보다 우수하고 특히 부하변동과 같은 외란에 강인함을 알 수 있었으며, 정상상태 오차가 현저히 감소하여 정밀한 속도제어가 가능함을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1474-1477
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• 2005

This paper is proposed maximum torque per ampere of induction motor using fuzzy-neural networks controller. Operation of maximum torque per ampere is achieved when, at a given torque and speed, the slip frequency is adjusted to that so that the stator current amplitude is minimized. This paper introduces a induction motor drive system with fuzzy-neural networks controller. A neural network-based architecture is described for fuzzy logic control. The characteristic rule and their membership function of fuzzy system are represented as the processing nodes in the neural network structure. This paper is proposed the analysis as well as the simulation results to verify the effectiveness of the new method.

• 제어로봇시스템학회논문지
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• 제10권2호
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• 한국항해학회지
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• 제21권3호
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• pp.55-66
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• 1997

In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.494-494
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• 2000

This paper demonstrates that the largest Lyapunov exponent $\lambda$ of recurrent neural networks can be controlled by a gradient method. The method minimizes a square error $e_{\lambda}=(\lambda-\lambda^{obj})^2$ where $\lambda^{obj}$ is desired exponent. The $\lambda$ can be given as a function of the network parameters P such as connection weights and thresholds of neurons' activation. Then changes of parameters to minimize the error are given by calculating their gradients $\partial\lambda/\partialP$. In a previous paper, we derived a control method of $\lambda$via a direct calculation of $\partial\lambda/\partialP$ with a gradient collection through time. This method however is computationally expensive for large-scale recurrent networks and the control is unstable for recurrent networks with chaotic dynamics. Our new method proposed in this paper is based on a stochastic relation between the complexity $\lambda$ and parameters P of the networks configuration under a restriction. Then the new method allows us to approximate the gradient collection in a fashion without time evolution. This approximation requires only $O(N^2)$ run time while our previous method needs $O(N^{5}T)$ run time for networks with N neurons and T evolution. Simulation results show that the new method can realize a "stable" control for larege-scale networks with chaotic dynamics.

• Journal of Communications and Networks
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• 제16권4호
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• pp.371-377
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• 2014

Localized topology control is attractive for obtaining reduced network graphs with desirable features such as sparser connectivity and reduced transmit powers. In this paper, we focus on studying how to prolong network lifetime in the context of localized topology control for wireless multi-hop networks. For this purpose, we propose an energy efficient localized topology control algorithm. In our algorithm, each node is required to maintain its one-hop neighborhood topology. In order to achieve long network lifetime, we introduce a new metric for characterizing the energy criticality status of each link in the network. Each node independently builds a local energy-efficient spanning tree for finding a reduced neighbor set while maximally avoiding using energy-critical links in its neighborhood for the local spanning tree construction. We present the detailed design description of our algorithm. The computational complexity of the proposed algorithm is deduced to be O(mlog n), where m and n represent the number of links and nodes in a node's one-hop neighborhood, respectively. Simulation results show that our algorithm significantly outperforms existing work in terms of network lifetime.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1169-1172
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• 1996

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality". The control law has been demonstrated for a velocity input type motor identified by a genetic algorithm called GENOCOP.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.40.1-40
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• 2001

In this paper, control theoretic approaches are proposed to guarantee QoS (Quality of Series) such as packet delay and packet loss of real-time traffic in high-speed communication network. Characteristics of variable rate real-time traÆc in communication networks are described. The mathematical model describing networks including source and destination nodes are suggested. By a traffic control mechanism, it is shown that worst-case end-to-end transfer delay of traffic can be controlled and packet loss can be prevented. The simulation shows results of delay control and buer level control to raise QoS in realtime traffic.