• 한국군사과학기술학회지
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• 제11권5호
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• pp.49-57
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• 2008

In this paper, we suggested a methodology that can be used by an agent to learn models of other agents in a multi-agent system. To construct these model, we used influence diagram as a modeling tool. We present a method for learning models of the other agents at the decision nodes, value nodes, and chance nodes in influence diagram. We concentrated on learning of the other agents at the value node by using neural network learning technique. Furthermore, we treated anti-air units in anti-air defense domain as agents in multi. agent system.

• 한국정밀공학회지
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• 제20권6호
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• pp.62-70
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• 2003

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is 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 RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.474-474
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• 2000

This paper shows that a system with two-link arm can obtain hand reaching movement to a target object projected on a visual sensor by reinforcement learning using a layered neural network. The reinforcement signal, which is an only signal from the environment, is given to the system only when the hand reaches the target object. The neural network computes two joint torques from visual sensory signals, joint angles, and joint angular velocities considering the urn dynamics. It is known that the trajectory of the voluntary movement o( human hand reaching is almost straight, and the hand velocity changes like bell-shape. Although there are some exceptions, the properties of the trajectories obtained by the reinforcement learning are somewhat similar to the experimental result of the human hand reaching movement.

• 전자공학회논문지B
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• 제28B권12호
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• pp.27-37
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• 1991

In this paper, the novel PLD (Preceding Layer Driven) MLP (Multi Layer Perceptron) neural network model and its learning algorithm is described. This learning algorithm is different from the conventional. This integer weights and hard limit function are used for synaptic weight values and activation function, respectively. The entire learning process is performed by layer-by-layer method. the number of layers can be varied with difficulty of training data. Since the synaptic weight values are integers, the synapse circuit can be easily implemented with CMOS. PLD MLP neural network was applied to English Characters, arbitrary waveform generation and spiral problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.768-770
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• 1996

This paper propose neural network which is one of self-organizing techniques. It is composed neural network controller as input signal is error and change of error which is optimal output, and is learned system by using a error back-propagation learning algorithm is one of error mimizing learning methods. In order to achieve practical real time control reduce on learning time, it is applied to load-frequency control of nonlinear power system with using a moment learning method. It is described in such a case considering constraints for a rate of increace generation-rate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1344-1346
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• 1996

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.

• 정보학연구
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• 제9권1호
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• pp.55-63
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• 2006

In this paper, a Hangul recognition system using of Kohonen Network in the phoneme separation and learning is proposed. A Hangul consists of phoneme that are consists of strokes. The phoneme recognition and separation are very important in the recognition of character. So, the phonemes which mismatching has been happened are correctly separated through the learning of neural networks. also, learning rate($\alpha$) adjusted according to error, in order to solved that its decreased the number of iteration and the problem of local minimum, adaptively.

• 대한전기학회논문지
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• 제39권12호
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.463-468
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• 2002

In this paper, we proposed a recurrent time delayed neural network controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network controller.