• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.10
• /
• pp.1779-1785
• /
• 2006

This paper presents a method of virtual robot arm control by EMG pattern recognition using the proposed hybrid system. The proposed hybrid system is composed of the LVQ and the SOFM, and the SOFM is used for the preprocessing of the LVQ. The SOFM converts the high dimensional EMG signals to 2-dimensional data. The EMG measurement system uses three surface electrodes to acquire the EMG signal from operator. Six hand gestures can be classified sufficiently by the proposed hybrid system. Experimental results are presented that show the effectiveness of the virtual robot arm control by the proposed hybrid system based classifier for the recognition of hand gestures from EMG signal patterns.

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

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.6
• /
• pp.995-1001
• /
• 1995

The structure of a typical pattern recognition consists a pre-processing, a feature extraction(algorithm) and classification or recognition. In classification, when widely varying patterns exist in same category, we need the clustering which organize the similar patterns. Clustering algorithm is two approaches. Firs, statistical approaches which are k-means, ISODATA algorithm. Second, neural network approach which is T. Kohonen's LVQ(Learning Vector Quantization). Nikhil R. Palet al proposed the GLVQ(Generalized LVQ, 1993). This paper suggest the efficient feature extraction methods of handwritten numerals in GLVQ clustering network. We use the handwritten numeral data from 21's authors(ie, 200 patterns) and compare the proportion of misclassified patterns for each feature extraction methods. As results, when we use the projection combination method, the classification ratio is 98.5%.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.7
• /
• pp.800-804
• /
• 2005

This paper presents a fuzzy learning rule which is the fuzzified version of LVQ(Learning Vector Quantization). This fuzzy learning rule 3 uses fuzzy learning rates. instead of the traditional learning rates. LVQ uses the same learning rate regardless of correctness of classification. But, the new fuzzy learning rule uses the different learning rates depending on whether classification is correct or not. The new fuzzy learning rule is integrated into the improved IAFC(Integrated Adaptive Fuzzy Clustering) neural network. The improved IAFC neural network is both stable and plastic. The iris data set is used to compare the performance of the supervised IAFC neural network 3 with the performance of backprogation neural network. The results show that the supervised IAFC neural network 3 is better than backpropagation neural network.

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

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1996.10a
• /
• pp.76-79
• /
• 1996

본 논무에서는 한국어 음소를 대상으로 Kohonen 이 제안한 LVQ2 방법의 결저을 보완한 MLVQ2 방법으로 인식실험을 행하고 MLVQ2 알고리즘의 유효성을 검토하고자 한다. 인식실험을 위한 음성자료는 ETRI 611단어로부터 추출한 49음소를 사용하였다. 그리고 인식실험에 있어서는 먼저 파열음을 대상으로 학습회수, 표준패턴의 수, 샘플수에 따른 인식률의 변화를 조사하였으며, 이 결과 표준패턴의 수 15개, 학습회수 10회 이하, 샘플 수 3000 개일 경우가 가장 좋은 인식률을 보였다. 이 결과를 참고로 음소군별 인식실험 결과 모음 69.11%, 파열음 74.69%, 마찰음 및 파찰음 86.31%비음 및 유음 74.51%의 평균 인식률을 얻었다. 또한 , 한국어 49음소 전음소에 대한 인식실험 결과 71.2%의 인식률 얻어 MLVQ2의 유효성을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.11a
• /
• pp.378-381
• /
• 2002

In this paper, we proposed an electromyography(EMG) based control method of a virtual robot arm as an adaptive human supporting system or remote control system, which consists of an shoulder control part, elbow control part, and wrist control part. The system uses four surface electrodes to acquire the EMG signal from operator. It is shown from the experiments that the EMG patterns during arm motions can be classified sufficiently by using SOM and LVQ. The interface system based on PC environment is constructed to 3-D graphic user interface(GUI) program. Experimental results show that proposed method obtains approximately 94 percent of success in classification.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.255-262
• /
• 1998

This paper presents a translation, rotation and scale invariant methodology for the recognition of closed planar shape images using the bispectrum of a contour sequence and the learning vector quantization(LVQ) neural classifier. The contour sequences obtained from the closed planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The higher order spectra based on third order cumulants is applied to tihs contour sample to extract fifteen bispectral feature vectors for each planar image. There feature vector, which are invariant to shape translation, rotation and scale transformation, can be used to represent two0dimensional planar images and are fed into a neural network classifier. The LVQ architecture is chosen as a neural classifier because the network is easy and fast to train, the structure is relatively simple. The experimental recognition processes with eight different hapes of aircraft images are presented to illustrate the high performance of this proposed method even the target images are significantly corrupted by noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2006.05a
• /
• pp.800-803
• /
• 2006

In this paper, we propose a method for improving the performance of the face recognition using a hybrid neural network. The propose method focused on improving face recognition technique using SOM and LVQ. In order to verify the effectiveness of the proposed method, we performed simulations on face database supplied ORL. The results show that the proposed method considerably improves on the performance of the eigenface, hidden markov model, multilayer neural network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.171-175
• /
• 1997

본 논문에서는 LVQ(Learning Vector Quentization) 신경회로망의 새로운 가중치 초기화법을 제안하고 이를 얼굴인식 시스템에 적용하였다. 제안한 방법은 초기가중치를 패턴 결정 경계면 주변에 설정함으로써 인식율을 높이는 방법이다. 얼굴인식의 특징 추출 방법으로서는 주성분 분석, 모멘트, 푸리에 기술자, 모멘트+주성분 분석 및 푸리에 기술자+주성분 분석 등을 사용하여 실험하였으며, 인식부의 LVQ 신경회로망에 제안된 방법을 적용하여 기존의 방법과 비교 실험하였다. 실험 결과 초기가중치를 최초 패턴으로 가지는 경우, 평균값을 취하는 경우, 랜덤하게 사용하는 경우 등에 비해서 우수한 인식율을 보임을 알 수 있었다.