• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.24.2-24
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• 2001

In this paper, the authors present a row dynamic route planning by Q-learning. The proposed algorithm is executed in a cellular automation based traffic simulator, which is also newly created. In Vehicle Information and Communication System(VICS), which is an active field of Intelligent Transport System(ITS), information of traffic congestion is sent to each vehicle at real time. However, a centralized navigation system is not realistic to guide millions of vehicles in a megalopolis. Autonomous distributed systems should be more flexible and scalable, and also have a chance to focus on each vehicles demand. In such systems, each vehicle can search an own optimal route. We employ Q-learning of the reinforcement learning method to search an optimal or sub-optimal route, in which route drivers can avoid traffic congestions. We find some applications of the reinforcement learning in the "static" environment, but there are ...

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.3
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• pp.284-292
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• 1997

This paper is concerned with position control of direct drive robots. The proposed algorithm consists of the feedback controller and neural networks. Mter the completion of learning, the output of the feedback controller is nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum retuning of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the con¬trolled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the position control of a parallelogram link-type direct drive robot.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.712-715
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• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.121-126
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.393-402
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• 1990

An iterative learning contol method is presented for a class of linear periodic systems, in which a parameter estimator of the system together with an inverse system model is utilized to generate the control signal at each iteration. A convergence proof is given and two numerical examples are illustrated to show the validities of the algorithm. In particular, it is shown that the method is useful for the continuous path control of robot manipulators.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.764-767
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.103.2-103
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• 2002

1. Introduction 2. Structure of BNN 3. Decision of weight value and threshold value 4. Principle of Extension in the ETL algorithm 5. Approximation problem of one circular region 6. Problem of synthetic image having four class 7. Conclusion

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.111.3-111
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• 2002

$\textbullet$introduction $\textbullet$CO-evolution Algorithm $\textbullet$Subsumption Architecture $\textbullet$Neural Network $\textbullet$Khepera Robot