• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.37-42
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• 1992

This Introductory talk of neural networks broadly covers the concepts, basic theory and some example problems. Neural network methods are compared with the AI technology. The structure, functionality as well as learning paradigms of the neural networks are introduced.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1012-1016
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• 1991

An effective visual servo control system for robotic manipulators based on neural networks is proposed. For this control system, firstly, one neural network is used to learn the mapping relationship between the robot's joint space and the video image space. However, in the proposed control scheme, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Secondly, an adaptive Adaline network is used to identify the dynamics of the robot and also to generate the proper torque commands. Computer simulation has been performed indicating its superior performance. As far as the authors know, this is the first time attempt of the use of neural networks for a visual servo control of robots that compensates for their changing dynamics.

• 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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• 1992.10b
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• pp.564-568
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• 1992

A neural optimization network is designed to solve the collsion-free inverse kinematics problem for redundant robot manipulators under the constraints of joint limits, maximum velocities and maximum accelerations. And the fuzzy rules are proposed to determine the weightings of neural optimization networks to avoid the collision between robot manipulator and obstacles. The inputs of fuzzy rules are the resultant distance, change of the distance and sum of the changes. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision avoidance of each joint. To show the validities of the proposed method computer simulation results are illustrated for the redundant robot with three degrees of freedom,

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1226-1231
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• 2008

A design of attitude controller for a tiltrotor is presented augmenting L1 adaptive control, neural networks, and feedback linearization. The neural networks compensate for the modeling error caused by the lack of knowledge of tiltrotor dynamics while the L1 adaptive control allows high adaptation gains in adaptation laws thereby, satisfying tracking performance requirement. The efficacy of this control methodology is illustrated in high-fidelity nonlinear simulation of a tiltrotor by flying the tiltrotor in different flight modes from where the L1 adaptive controller with neural networks is originally designed for.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.675-678
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• 1997

In this research we continue the study of nuclear power plant steam generator's intelligent modeling. This model represents the input-output behavior and is a preliminary stage for intelligent control. Among many intelligent models available, we study neural network models that have been proven as universal function approximators. We select multilayer perceptrons, circular backpropagation networks, piecewise linearly trained networks and recurrent neural networks as the candidates for the steam generator's intelligent models. We take the input-output pairs from steam generator's reference model and train the neural network models. We validate trained neural network models as intelligent models of steam generator.

• Proceedings of the KIEE Conference
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• 2002.11d
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• pp.94-96
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• 2002

This paper is deal with the method of redesign for optimum thrust model using Neural-Networks in Permanent Magnet Linear Synchrous Motor(PMLSM). This method is saved time compared with design method using only Finite Element Method(FEM). In this paper data sets for training Neural-Networks obtained using 2D FEM. To confirm the validity of the data sets for training Neural-Networks optimum values of that Is compared with results of FEM. And then. this method is verified that it could be applied to the design for Slotless type PMLSM.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.158-164
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• 2002

This paper proposes the optimization of ultrasonic signal recognition in ferrite carbon steel weld zone using neural networks. For these purposes, the ultrasonic signals for defects as porosity, incomplete penetration and slag inclusion in the weld zone are acquired in the type of time series data. And then their applications evaluated feature extraction based on the time-frequency-attractor domain(peak to peak, rise time, rise slope, fall time, fall slope, pulse duration, power spectrum, and bandwidth) and attractor characteristics (fractal dimension and attractor quadrant) etc. The proposed neural networks system in this study can enhances performance of ultrasonic signal recognition.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.1
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• pp.91-97
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• 1996

This paper deals with a cold forging design system by which designers can determine desirable plans of cold forging design even if they have little experience. In this system, neural networks are used to transform qualitative knowledges to quantitative knowledges. The neural network is learned with three parts which are most important in cold forging design - undercut, narrow hole, sharp corner. The capabilities of the system are illustrated through an example of forging design.