• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1168-1171
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• 2003

The visuo-motor map is based on the Kohonen's self-organizing map. The map is learned the relation of the end effecter coordinates and the joint angles. In this paper, a 3 d-o-fmanipulator which moves in the 2D space is targeted. A CCD camera is set beside the manipulator, and the end effecter coordinates are given from the image of a manipulator. As a result of learning, the end effecter can be moved to the destination without exact teaching.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.287-290
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• 2001

Bishop이 제안한 generative Topographic Mapping(GTM)은 Kohonen이 제안한 자율 학습 신경망인 Self Organizing Maps(SOM)의 확률적 버전이다. 본 논문에서는 이러한 GTM 모형에 베이지안 추론을 결합하여 작은 오분류율을 가지는 분류 알고리즘인 베이지안 GTM(Bayesian GTM)을 제안한다. 이 방법은 기존의 GTM의 빠른 계산 처리 능력과 베이지안 추론을 이용하여 기존의 분류 알고리즘보다 우수한 결과가 나타남을 실험을 통하여 확인하였다.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.2
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• pp.65-76
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• 1994

The most effective computer algorithms for facility layout that have been found are mainly based on the improvement heuristic such as CRAFT. In this paper, we present a new algorithm which is based on the Kohonen neual network. The algorithm firstly forms a self-organizing feature map where the most important similarity relationships among the facilities are converted into their spatial relationships. A layout is then obtained by a minor adjustment to the map. Some simulation results are given to show the performance of the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1468-1473
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• 2005

In this paper, I proposed the INTelligent Agent System by Kohonen's Self Organizing Neural Network (INTAS). INTAS creates each user's profile from the information. Based on it, learning community grouping suitable to each individual is automatically executed by using unsupervised learning algorithm. In INTAS, grouping and learning are automatically performed on real time by multiagents, regardless of the number of learners. A new framework has been proposed to generate multiagents, and it is a feature that efficient multiagents can be executed by proposing a new negotiation mode between multiagents..

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.11
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• pp.1857-1866
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• 1989

In this paper, an algorithm to update a codebook using a neural network in consecutive images, is proposed. With the Kohonen's self-organizing feature map, we adopt the iterative technique to update a centroid of each cluster instead of the unsupervised learning technique. Because the performance of this neural model is comparable to that of the LBG algorithm, it is possible to update the codebooks of consecutive frames sequentially in TV and to realize the hardwadre on the real-time implementation basis.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.768-772
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• 1991

The Self Creating and organizing (SCO) is a new architecture and one of the unsupervized learning algorithm for the artificial neural network. SCO begins with only one output node which has a sufficiently wide response range, and the response ranges of all the nodes decrease with time. Self Creating and Organizing Neural Network (SCONN) decides automatically whether adapting the weights of existing node or creating a new node. It is compared to the Kohonen's Self Organizing Feature Map (SOFM). The results show that SCONN has lots of advantages over other competitive learning architecture.

• Information and Communications Magazine
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• v.11 no.6
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• pp.68-84
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• 1994

입력된 영상정보로부터 3차원 물체를 찾아내거나 인식하는 것은 컴퓨터에게 인간과 같은 능력을 부여하기 위한 가장 기본적인 과제라 할 수 있다. 본 글에서는 우선 3차원 정보를 취득하는 다양한 방법을 간단히 살펴보고 이에서 받아들인 3차원 정보를 가지고 어떻게 3차원 문체의 형태를 서술하는가를 기존의 발표된 많은 문헌에 근거해설 요약해 보았다. 3차원 물체의 인식기법을 살펴보고 신경망을 이용한 3차원 물체인식 특히 Kohonen Map을 통한 3차원 인식을 중점적으로 다루었다. 마지막으로 보행 로보트의 주행에 필요한 3차원 정보 취득 및 그 이용 방법을 제시하였다.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.38-43
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• 1991

In order to cluster the Input pattern neatly, some neural network modified from Kohonen's self organizing feature map is introduced and Korean phoneme recognition experiments are performed using the modified self organizing feature map(MSOFM) and the auditory model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.371-376
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• 1994

This paper presents an on-line modeling method approach for the machine condition. the machine condition is continuously monitored with a sensor such as, a vibration, a current, an acoustic emission (AE) sensor. In this study, neural network modeling by radial basis function is designed for analysis a prediction error. An on-line learning algorithm is designed using the RLS(recursive least square) estimation and the existing clustering method of Kohonen neural network. Experimental results show that the proposed RBNN modeling is suitable for predicting simulated data.

• The Journal of the Acoustical Society of Korea
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• v.12 no.1E
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• pp.71-77
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• 1993

패턴매칭 기법에 근거한 음성 인식 시스템은 크게 clustering 과정과 labeling 과정으로 구성된다. 본 논문에서는 Kohonen의 featrue map 알고리즘과 LVQ2 알고리즘을 각각 clusterer와 labeler로 하는 음소인식 시스템을 구성한다. 구성된 인식시스템의 성능을 향상시키기 위해서 수정된 LVQ2알고리즘(MLVQ2)을 제안한다. MLVQ2는 selective learning, LVQ2, perturbed LVQ2 그리고 기존의 LVQ2의 4단계 학습과정으로 구성된다. 제안된 음소 인식 알고리즘의 성능을 평가하기 위하여 LVQ2와 MLVQ2를 각각 사용하여 6가지의 한국어 음소군에 대한 feature map을 만든다. 음소인식 실험결과, LVQ2와 MLVQ2를 사용하는 경우 각각 60.5%와 65.4%의 인식률을 얻을 수 있었다.