• Journal of Korea Multimedia Society
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• v.7 no.7
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• pp.877-885
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• 2004

In this paper, we propose an enhancement shot change detection method using the neural net and the robust feature selection out of multiple features. The previous shot change detection methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using robust features, which are supplementary each other, rather than using single feature. In this paper, we use the typical CART (classification and regression tree) of data mining method to select the robust features, and the backpropagation neural net to determine the threshold of the each selected features. And to evaluation the performance of the robust feature selection, we compare the proposed method to the PCA(principal component analysis) method of the typical feature selection. According to the experimental result. it was revealed that the performance of our method had better that than the PCA method.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.345-355
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• 1998

The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper two types of robust backpropagation algorithms are discussed both from a theoretical point of view and in the case studies of nonlinear regression function estimation and handwritten Korean character recognition. For future research we suggest Bayesian learning approach to neural networks and compare it with two robust backpropagation algorithms.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• JSTS:Journal of Semiconductor Technology and Science
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• v.6 no.3
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• pp.182-188
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• 2006

An artificial neural network model for the microwave characteristics of an InGaAs/InP hemt for 70 nm gate length has been developed. The small-signal microwave parameters have been evaluated to determine the transconductance and drain-conductance. We have further investigated the frequency characteristics of the device. The neural network training have been done using the three layer architecture using Levenberg-Marqaurdt Backpropagation algorithm. The results have been compared with the experimental data, which shows a close agreement and the validity of our proposed model.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.41-48
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• 2003

This paper presents an indirect adaptive neuro controller using modified chaotic neural networks(MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new Dynamic Backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and the indirect adaptive neuro controller. The simulation results show good performances, since the MCNN has robust adaptability to nonlinear dynamic system.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.124-129
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• 2007

For seismic imaging and inversion, the inverted image depends on how we define the objective function. ${\ell}^1$-norm is more robust than ${\ell}^2$-norm. However, it is difficult to apply the Newton-type algorithm directly because the partial derivative for ${\ell^1$-norm has a singularity. In our paper, to overcome the difficulties of singularities, Huber function given by hybrid ${\ell}^1/{\ell}^2$-norm is used. We tested the robustness of our new object function with several noisy data set. Numerical results show that the new objective function is more robust to band limited spiky noise than the conventional object function.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1995.10a
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• pp.96-100
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• 1995

In this paper an induction motor control using fuzzy controller and neural network adptive observer is studied. The proposed observer which comprises neural network flux observer which comprises neural network flux observer and neural network torque observer is trained to learn the flux dynamics and torque dynamics and subjected to further on-line training by means of a backpropagation algorithem. Therefore it has been shown that the robust control of induction motor neglects the rotor time constant variations

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.635-641
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• 2016

In many industrial applications that use the electric motors robust controllers are needed. The method using a neural network in order to design a robust controller when a disturbance occurs is studied. Backpropagation algorithm, which is used in a conventional neural network controller is used in many areas, but when the number of neurons in the input layer, hidden layer and output layer of the neural network increases the processing speed of the learning process is slow. In this paper an adaptive PI(Proportional and Integral) controller based on CTRNN(Continuous Time Recurrent Neural Network) for permanent magnet synchronous motors is presented. By varying the load and the speed the validity of the proposed method is verified through simulation and experiments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.39-50
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• 2006

The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.