• 한국산업융합학회 논문집
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• 제6권4호
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• pp.393-399
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• 2003

In this paper, a self-organizing fuzzy logic controller with hybrid structure is proposed. The structure of the proposed method is composed of a basic fuzzy logic controller and the FARMA SOC(Fuzzy Autoregressive Moving Average Self-organizing Controller). The self-organizing cntroller with hybrid structure has advantage over the FARMA controller as follows. The proposed controller improves poor performance due to the lack of I/O data to calculate predictive output. I executed some computer simulations on the regulation problem of an inverted pendulum system and compared the results of the proposed method with those of the FARMA SOC method.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1052-1057
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• 1994

Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.

• 정보처리학회논문지A
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• 제13A권6호
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• pp.501-510
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• 2006

자기 조직적 우수 피어 링 검색 기법에서는 기존의 비구조적 피어-투-피어 시스템에서 성능이 우수한 피어들이 자기 조직적으로 우수 피어링을 형성하고, 모든 피어가 키의 광고 및 검색에 이 링을 이용하게 함으로써 검색 성능을 향상 시켰다. 그러나 이 기법에서는 우수 피어의 비율을 고정된 값으로 유지하므로, 본 논문에서는 입자 군집 최적화(PSO)를 이용하여, 동적인 환경의 변화에 적응적으로 우수 피어의 비율을 최적의 값으로 변화시킴으로써, 기존의 자기 조직적 우수 피어 링 검색 기법의 성능을 더욱 향상시켰다. 시뮬레이션을 통하여 고정 우수 피어 링과 자기 조직적 우수 피어 링, 그리고 적응적 우수 피어 비율 조절 기법을 비교하여, 제안하는 기법이 검색 성공률을 월등히 향상시킴을 확인하였다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.170-177
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• 2003

Gene expression data are the quantitative measurements of expression levels and ratios of numberous genes in different situations based on microarray image analysis results. The process to draw meaningful information related to genomic diseases and various biological activities from gene expression data is known as gene expression data analysis. In this paper, we present a hierarchical clustering method of gene expression data based on self organizing map which can analyze the clustering result of gene expression data more efficiently. Using our proposed method, we could eliminate the uncertainty of cluster boundary which is the inherited disadvantage of self organizing map and use the visualization function of hierarchical clustering. And, we could process massive data using fast processing speed of self organizing map and interpret the clustering result of self organizing map more efficiently and user-friendly. To verify the efficiency of our proposed algorithm, we performed tests with following 3 data sets, animal feature data set, yeast gene expression data and leukemia gene expression data set. The result demonstrated the feasibility and utility of the proposed clustering algorithm.

• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1100-1106
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• 2004

In this report, a self-organizing fuzzy neural network is proposed to control chemical feeding, which is one of the most important problems in water treatment process. In the case of the learning according to raw water quality, the self-organizing fuzzy network, which can be driven by plant operator, is very effective, Simulation results of the proposed method using the data of water treatment plant show good performance. This algorithm is included to chemical feeder, which is composed of PLC, magnetic flow-meter and control valve, so the intelligent control of chemical feeding is realized.

• 산업기술연구
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• 제24권A호
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 춘계종합학술대회
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• pp.402-405
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• 2000

본 논문에서는 근육의 움직임에 의해 유발되는 전기적 신호인 근전도(EMC) 신호를 신경회로망을 통해 분류하여 인체의 움직임을 파악하는 방법을 제안한다 신호분류를 위한 신경회로망으로 학습에 의해 스스로 출력뉴런을 구성하는 SOM을 사용하였으며, 실험과 시뮬레이션을 통해 제안한 방식의 효과를 확인하였다.

• 전자공학회논문지SC
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• 제38권4호
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• pp.31-36
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• 2001

본 논문에서는 근육의 움직임에 의해 유발되는 전기적 선호인 근전도(EMG) 신호를 신경회로망을 통해 분류하여 인체의 움직임을 파악하는 방법을 제안한다. 신호분류를 위한 신경회로망으로 학습에 의해 스스로 출력뉴런을 구성하는 SOM을 사용하였으며， 기존의 방식과 다르게 전처리 과정 없이 신호자세를 SOM의 입력으로 사용하여 패턴을 분류하는 간단한 방식이다. 실험과 시뮬레이션을 통해 제안한 방식의 유용성을 확인하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.166-173
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• 2005

Preserving image features, edges and details in the process of impulsive noise filtering is an important problem. To avoid image blurring, only corrupted pixels must be filtered. In this paper, we propose an effective impulse noise detection method using Self-Organizing Neural Network(SONN) which applies median filter selectively for removing random-valued impulse noises while preserving image features, edges and details. Using a $3\times3$ window, we obtain useful local features with which impulse noise patterns are classified. SONN is trained with sample image patterns and each pixel pattern is classified by its local information in the image. The results of the experiments with various images which are the noise range of $5-15\%$ show that our method performs better than other methods which use multiple threshold values for impulse noise detection.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.