• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.251-267
• /
• 2002

Self Organizing Maps(SOM) by Kohonen is very fast algorithm in neural networks. But it doesn't show sure rules of training results. In this paper, we introduce to Bayesian Learning for Self Organizing Maps(BLSOM) which combines self organizing maps with Bayesian learning. So it supports explanatory power of models and improves prediction. BLSOM has global optima anywhere but SOM has not. This is proved by experiment in this paper.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 2000.11a
• /
• pp.25-35
• /
• 2000

A huge amount of financial information in large databases makes performance comparisons among organizations difficult or at least very time-consuming. This paper investigates whether neural networks in the form of self-organizing maps can be effectively employed to perform a competitive benchmarking in large databases. By using self-organizing maps, we expect to overcome problems associated with finding appropriate underlying distributions and functional forms of underlying data. The method also offers a way of visualizing the results. The database in this study consists of annual financial reports of 100 biggest Korean companies over the years 1998, 1999, and 2000.

• Journal of the Korean Society of Industry Convergence
• /
• v.6 no.1
• /
• pp.37-42
• /
• 2003

In this paper a self-organizing fuzzy system with rule pruning is proposed. A conventional self-organizing fuzzy system having only rule generation has a drawback in generating many slightly different rules from the existing rules which results in increased computation time and slowly learning. The proposed self-organizing fuzzy system generates fuzzy rules based on input-output data and prunes redundant rules which are caused by parameter training. The proposed system has a simple structure but performs almost equivalent function to the conventional self-organizing fuzzy system. Also, this system has better learning speed than the conventional system. Simulation results on several numerical examples demonstrate the performance of the proposed system.

• Korean Journal of Social Welfare
• /
• v.64 no.1
• /
• pp.5-30
• /
• 2012

This study was carried out to understand the mean of neighbourhood and community organizing model on lived experiences of their organizing and members which had changed. The methods of collecting data was progressed in depth interview. According to study questions and analysis challenges based on theory, we analyzed texts. outcome of study we understood experiences of their organizing and members which had changed. In changing phases of organizational dimension, meaning of 'the discovery of the community problem' was extracted. In this time, organization's members set the goal and experience qualitative and quantitative changes of organization. On the other hand, changing phases of individual dimension were followed, which are 'escaping from locked life', 'reconstruction of self image', 'reconstructing the meaning of both family and self-concept' as well as 'reconstructing the meaning of both neighbourhood and self-concept'. Conclusively, we suggested practical implication, which might increase the effect of neighbourhood and community organizing model.

• Journal of IKEEE
• /
• v.12 no.4
• /
• pp.217-224
• /
• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

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

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.61-64
• /
• 2001

본 논문은 Self Organizing Map을 이용한 한국어의 모음 10개를 인식하는 것을 다루고 있다. 분류기로서 우수한 성능을 보이고 있는 Self Organizing Map의 출력 층을 2차원으로 구성하여 짧은 시간 간격으로 주파수 도메인에서 벡터화 되어진 음성을 입력 층에 인가하여 유사한 출력 층의 분포를 이용하여 모음 10개를 인식하는 분류기로서의 가능성을 보여줄 것이다.

• The Journal of the Korea Contents Association
• /
• v.17 no.9
• /
• pp.159-172
• /
• 2017

This study examined the predisposing, enabling and need factors, based on Andersen's behavioral model, affecting intention to use community organizing services at a community welfare center. 725 people aged 20 and older who lived in Gwang-jin-gu participated in this study and multiple linear regression analyses were utilized to analyze the deputation. The results demonstrate that intention to use is primarily affected by predisposing factors, age and duration of education. Female and more educated people were significantly associated with the intention to use community organizing services. Enabling factors, reputation and image of the community welfare center significantly affect intention to use community organizing services. People are more likely to use community organizing services when there is higher reputation of the community welfare center and better image of the community welfare center. Finally, intention to use is primarily affected by welfare consciousness, a need factor. People who have liberal welfare consciousness are more likely to use community organizing services. These findings provide implications and suggestions for increasing intention to use community organizing services at a community welfare center.

• East Asian mathematical journal
• /
• v.38 no.1
• /
• pp.21-31
• /
• 2022

This paper is concerned with the optimal control problem associated to the self-organizing target detection model in 1D domains. That is, we show the global existence of weak solution and the existence of optimal control.

• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.2
• /
• pp.108-114
• /
• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.