• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.848-850
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• 2000

본 논문에서는 신경회로망을 이용하여 On-Line상에서 EEG(Electroencephalogram) 신호를 분류하는 방법을 제안한다. EEG 신호란 인간의 두뇌활동에서 발생하는 전기적 신호로서 고도의 비선형과 시변 특성을 지니고 있어 정량적인 분석이 어려운 신호로 여겨진다. 이를 분석하기 위해 본 논문에서는 입력 벡터들을 서브클래스로 분류하는 경쟁 레이어와 서브클래스를 모아 정해진 클래스를 선택하는 선형 레이어로 이루어진 LVQ (Learning Vector Quantization) 신경망을 구성하고 On-Line 분석결과를 제시한다. 이러한 On-line 분석방법은 EEG 신호를 실시간으로 분석하여 컴퓨터를 인간의 생각만으로 제어될 수 있는 BCI(Brain Computer Interface)의 구현에 사용될 것이다.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.241-252
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• 2000

In this paper, a translation, rotation and scale invariant system for the recognition of closed 2-D images using the bispectrum of a contour sequence and a weighted fuzzy classifier is derived and compared with the recognition process using one of the competitive neural algorithm, called a LVQ( Loaming Vector Quantization). The bispectrum based on third order cumulants is applied to the contour sequences of an image to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to the represent two-dimensional planar images and are fed into a weighted fuzzy classifier. The experimental processes with eight different shapes of aircraft images are presented to illustrate a relatively high performance of the proposed recognition system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.389-398
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• 2000

The popular methods to check the identity of individuals include passwords and ID cards. These conventional method for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.361-369
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• 1999

In this paper, we propose a new intelligent control system to be adapted for the characteristic of user who use the plant. The proposed intelligent control system is composed of the artificial neural network, the teaming vector quantization network. In order to verify the usefulness of the proposed system, we simulated in using Matlab.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.149-160
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• 1997

This paper is concerned with the problem of speaker-adaptive speech synthes is method using a mapped codebook designed by fuzzy mapping on FLVQ (Fuzzy Learning Vector Quantization). The FLVQ is used to design both input and reference speaker's codebook. This algorithm is incorporated fuzzy membership function into the LVQ(learning vector quantization) networks. Unlike the LVQ algorithm, this algorithm minimizes the network output errors which are the differences of clas s membership target and actual membership values, and results to minimize the distances between training patterns and competing neurons. Speaker Adaptation in speech synthesis is performed as follow;input speaker's codebook is mapped a reference speaker's codebook in fuzzy concepts. The Fuzzy VQ mapping replaces a codevector preserving its fuzzy membership function. The codevector correspondence histogram is obtained by accumulating the vector correspondence along the DTW optimal path. We use the Fuzzy VQ mapping to design a mapped codebook. The mapped codebook is defined as a linear combination of reference speaker's vectors using each fuzzy histogram as a weighting function with membership values. In adaptive-speech synthesis stage, input speech is fuzzy vector-quantized by the mapped codcbook, and then FCM arithmetic is used to synthesize speech adapted to input speaker. The speaker adaption experiments are carried out using speech of males in their thirties as input speaker's speech, and a female in her twenties as reference speaker's speech. Speeches used in experiments are sentences /anyoung hasim nika/ and /good morning/. As a results of experiments, we obtained a synthesized speech adapted to input speaker.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.988-998
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• 1999

This paper is to investigate the method of reducing the number of fuzzy rules with the help of LVQ. a large number of training patterns usually leads to a large set of fuzzy rules that require a large computer memory and take a long time to perform classification. so, in order to solve these problems, it is necessary to study to minimize the number of fuzzy rules. However, so as to minimize the performance degradation resulting from the reduction of fuzzy rules, fuzzy rules are generated after training the high-quality initial reference pattern. Through the simulation, we confirm that the proposed method is very effective.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.313-320
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• 1999

In this paper, a translation, rotation and scale invariant system for the classification of closed 2D images using the bispectrum of a contour sequence and the weighted fuzzy mean method is derived and compared with the classification process using one of the competitive neural algorithm, called a LVQ(Learning Vector Quantization). The bispectrun based on third order cumulants is applied to the contour sequences of the images to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images and are fed into an classifier using weighted fuzzy mean method. The experimental processes with eight different shapes of aircraft images are presented to illustrate the high performance of the proposed classifier.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.