• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.509-514
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• 2000

A structural control algorithm using CMAC(Cerebellar Model Articulation Controller) neural network is proposed Learning rule for CMAC is derived based on cost function. Learning convergence of CMAC is compared with MLNN(Multilayer Neural Network). Numerical examples are shown to verify the proposed control algorithm. Examples show that CMAC can be applicable to structural control with fast learning speed.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• The Journal of Information Technology
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• v.8 no.1
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• pp.119-124
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• 2005

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Kohonens Self-Organization Neural networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to clustering of the random weight. The result shows improved learning rate about 42~55% ; less iteration counts with correct answer.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1585-1588
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• 1997

In this paper, divide total workspace of robot manipulator into several subspaces and construct PID controller ineach subspace. Using EvolutionSTrategy we optimize the gains of PID controller in each subspace. But the gains may have a large difference on the boundary of subspaces, which can cause bad oscillatory performance. So we use Aritificial Neural Network to have continuous gain curves htrough the entire subspaces. Simualtion results show that the proposed method is quite useful.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.17-20
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• 2001

In this paper, we design a nonlinear adaptive controller using wavelet neural network. The method proposed in this paper performs for a nonlinear system with unknown parameters, identification with using a wavelet neural network, and then a nonlinear adaptive controller is designed with those identified informations. The advantage of the proposed control method is simple to design a controller for unknown nonlinear systems, because we use the identified informations and design parameters are positioned within a negative real part of s-plane. The simulation results showed the effectiveness of proposed controller design method.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.112-114
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• 1991

This paper describes an application of neural network to diagnose defect of induction motor and investigates possibility to construct defect diagnosis system to be operated without special knowledge. For defect diagnosis, frequency spectrum of vibration is utilized. Learning method of applied neural network is back propagation.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.163-169
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• 2014

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.287-292
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we confirmed advantages/disadvantages of four algorithms and identified application methods of few algorithms.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.3
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• pp.315-320
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• 2009

The purpose of this study is to predict the roundness of Numerical Control Machining so that helps the operator to choose the right machining conditions to produce a product within the given error limits. Learning of neural network is Backpropagation theory. From this study, the base was set to setup the database to produce precisely machined product by predicting the rate of error in the fabrication facility which does not have the environment to analyze it.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.29-32
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• 2000

We proposed a neural network based “left shoulder”detector. The auto-associative neural network was trained with the “left shoulder”patterns obtained from the Korea Composite Stock Price Index, and then tested out-of-sample with a reasonably good result. A hypothetical investment strategy based on the detector achieved a return of 132% in comparison with 39% return from a buy and hold strategy