• Journal of Energy Engineering
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• v.6 no.1
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• pp.68-76
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• 1997

This paper presents a new neural network training algorithm which reduces the required training time considerably and overcomes many of the shortcomings presented by the conventional back-propagation algorithm. The algorithm uses a modified form of the back-propagation algorithm to minimize the mean squared error between the desired and actual outputs with respect to the inputs to the nonlinearities. Artificial Neural Network (ANN) model using the new algorithm is applied to forecast the short-term electric load. Inputs to the ANN are past loads and the output of the ANN is the hourly load forecast for a given day.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.953-955
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• 1995

Studied on identification of nonlinear system with unknown variables and adaptive control were successful. We need a mathmatical model when control a dynamic system using adaptive control technique, but it is very difficult due to its nonlinearity. In this paper, we described about performance improvement of error back-propagation algorithm and learning algorithm of non-linear dynamic system. We examined the proposed back-propagation learn algorithm for through an experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.109-116
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• 1998

In this paper, a back propagation neural network teaming algorithm based on the complex multilyer perceptron is represented for suppressing narrowband interference of the received signals in DS-SS mobile communication system. We proposed neural network adaptive correlator(NNAC) which has fast convergence rate and good performance with combining back propagation neural network and the receiver of DS-SS. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow-band interference and the co-channel interference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1372-1377
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• 2013

In this Paper, a back propagation neural network learning algorithm based on the complex multilayer perceptron is represented for controling and detecting interference of the received signals in cellular mobile communication system. We proposed neural network adaptive correlator which has fast convergence rate and good performance with combining back propagation neural network and the receiver of cellular. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow-band interference and the co-channel interference.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.26-35
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• 1998

This paper presents a visual inspection algorithm looking for solder joint defects of IC chips on PCBs (Printed Circuit Boards). In this algorithm, seven features are proposed in order to categorize the solder joints into four classes such as normal, insufficient, excess, and no solder, and optimal back-propagation network is determined by error evaluation which depend on the number of neurons in hidden and out-put layers and selection of the features. In the end, a good accuracy of classification performance, an optimal determination of network structure and the effectiveness of chosen seven features are examined by experiment using proposed inspection algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.542-546
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• 2009

In this Paper, a back propagation neural network learning algorithm based on the complex multilayer perceptron is represented for controling and detecting interference of the received signals in cellular mobile communication system. We proposed neural network adaptive correlator which has fast convergence rate and good performance with combining back propagation neural network and the receiver of cellular. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow - band interference and the co-channel interference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1095-1106
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• 1994

This paper presents a method for determining optimal grayscale function processing(FP) morphological filters under the least square (LMS) error criterion. The optimal erosion and dilation filters with a grayscale structuring element(GSE) are determined by minimizing the mean square error (MSE) between the desired signal and the filter output. It is shown that convergence of the erosion and dilation filters can be achieved by a proper choice of the step size parameter of the LMS algorithm. In an attempt to determine optimal closing and opening filters, a matrix representation of both opening and closing with a basis matrix is proposed. With this representation, opening and closing are accomplished by a local matrix operation rather than cascade operations. The LMS and back-propagation algorithm are utilzed for obtaining the optimal basis matrix for closing and opening. Some results of optimal morphological filters applied to 2-D images are presented.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.481-491
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• 2003

We present a near-optimal blind decision feedback equalizer (DFE) for Advanced Television Systems Committee digital television (DTV) receivers. By adopting a modified trellis decoder (MTD) with trace back depth of 1 for the decision device In the DFE, we obtain a hardware-efficient near-optimal blind DFE approaching to the optimal DFE which has no error propagation. The MTD uses absolute distance instead of Euclidean distance for computation of a path metric, resulting. In reduced computational complexity. Comparing to the conventional slicer, the MTD shows outstanding performance improvement of decision error probability and is comparable to the original trellis decoder using Euclidean distance. Reducing error propagation in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer.