• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.502-505
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• 2002

본 논문은 다치(MVL:Multiple Valued Logic) 신경망의 BP(Backpropagation) 학습 알고리즘을 이용하여 패턴 인식에 이용하는 방법을 제안한다. MVL 신경망을 이용하여 패턴 인식에 이용함으로서, 네트워크에 필요한 시간 및 기억 공간을 최소화할 수 있고 환경 변화에 적응할 수 있는 가능성을 제시하였다. MVL 신경망은 다치 논리 함수를 기반으로 신경망을 구성하였으며, 입력은 리터럴 함수로 변환시키고, 출력은 MIN과 MAX 연산을 사용하여 구하였고, 학습을 하기 위해 다치 논리식의 편 미분을 사용하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.397-404
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• 2002

In this paper, it is proposed wavelet neural network using the fuzzy concept with the fuzzy and the multi-resolution analysis(MRA) of wavelet transform. Also, it wishes to improve any nonlinear function learning approximation using this system. Here, the fuzzy concept is used the bell type fuzzy membership function. And the composition of wavelet has a unit size. It is used the backpropagation algorithm for learning of wavelet neural network using the fuzzy concept. It is used the multi-resolution analysis of wavelet transform, the bell type fuzzy membership function and the backpropagation algorithm for learning. This structure is confirmed to be improved approximation performance than the conventional algorithms from one dimension and two dimensions function through simulation.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.79-84
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• 1996

In this paper, adaptive correlator model using backpropagation neural network based on complex multilayer perceptron is presented for suppressing interference of narrow-band of direct sequence spread spectrum receiver in CDMA mobile communication systems. Recursive least square backpropagation algorithm with backpropagation error is used for fast convergence and better performance in adaptive correlator scheme. According to signal noise ratio and transmission power ratio, computer simulation results show that bit error ratio of adaptive correlator uswing backpropagation neural network improved than that of adaptive transversal filter of direct sequence spread spectrum considering of co-channel and narrow-band interference. Bit error ratio of adaptive correlator using backpropagation neural network is reduced about $10^{-1}$ than that of adaptive transversal filter where interference versus signal ratio is 5 dB.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1040-1047
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• 2011

This paper proposes a novel method based on error back propagation neural networks to fuse laser sensor data and ultrasonic sensor data for enhancing the accuracy of mapping. For navigation of single robot, the robot has to know its initial position and accurate environment information around it. However, due to the inherent properties of sensors, each sensor has its own advantages and drawbacks. In our system, the robot equipped with seven ultrasonic sensors and a laser sensor navigates to map two different corridor environments. The experimental results show the effectiveness of the heterogeneous sensor fusion using an error backpropagation algorithm for mapping.

• Composites Research
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• v.14 no.5
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• pp.26-37
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• 2001

After impact analysis of the composite cylindrical shells was performed. obtained outputs at 9 equally divided points of the shell were used as input patterns of the neural networks. Identification of impact loading characteristics was predicted simultaneously. Momentum backpropagation algorithm of neural networks which can modify the momentum coefficient and learning rate was developed and applied to identify the loading characteristics. Hidden layers of the backpropagation increased from 1 layer to 3 layers and trained the loading characteristics. Developed program with variable learning rate was converged close to real load characteristics under 1% error. Inverse engineering which identify the impact loading characteristics can be applicable to the composite laminated cylindrical shells with developed neural networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.258-261
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• 2007

This paper presents techniques to recognize music using back propagation network, one of the neural network algorithms, and to preprocess technique for music image. Music symbols and music notes are segmented by preprocessing such as binarization, slope correction, staff line removing, etc. Segmented music symbols and music notes are recognized by music note recognizing network and non-music note recognizing network. We proved correctness of proposed music recognition algorithm through experiments and analysis with various kind of musics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.32-41
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• 2008

The moving object tracking in vision based observation using video uses difference method between GMM(Gaussian Mixture Model) based background and present image. In the case of racking object using binary image made by threshold, the object is merged not by object information but by Cast-Shadow. This paper proposed the method that eliminates Cast-Shadow using backpropagation Neural Network. The neural network is trained by abstracting feature value form training image of object range in 10-movies and Cast-Shadow range. The method eliminating Cast-Shadow is based on the method distinguishing shadow from binary image, its Performance is better(16.2%, 38.2%, 28.1%, 22.3%, 44.4%) than existing Cast-Shadow elimination algorithm(SNP, SP, DNM1, DNM2, CNCC).

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.511-518
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• 2008

Medical diagnosis systems have been developed to make the knowledge and expertise of human experts more widely available, therefore achieving high-quality diagnosis. In this study, in order to support the diagnosis by the medical diagnosis system, we have preformed medical diagnosis inference three times: first by a neural network with the backpropagation algorithm, secondly by a discriminant analysis with all of the variables, and thirdly by a discriminant analysis with the selected variables. A discussion on comparison of these three methods has been provided.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.2
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• pp.65-70
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• 2003

In this study, researchers developed the estimative algorithm for artificial defect in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages : Crack, Delamination and Normal. According to the results, we were confirmed that estimative algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.488-492
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• 1993

This paper proposes a new approach to the optimization method of a blending process with neural network. The method is based on the error backpropagation learning algorithm for neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a system solver. A fuzzy membership function is used in parallel with the neural network to minimize the difference between measurement value and input value of neural network. As a result, we can guarantee the reliability and stability of blending process by the help of neural network and fuzzy membership function.