• 대한산업공학회지
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• 제31권3호
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• pp.257-264
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• 2005

This paper suggests a heuristic algorithm for the clustering problem. Clustering involves grouping similar objects into a cluster. Clustering is used in a wide variety of fields including data mining, marketing, and biology. Until now there are a lot of approaches using Self-Organizing Feature Maps(SOFMs). But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of k output-layer nodes, if they want to make k clusters. This approach has problems to classify elaboratively. This paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node's weight. We can find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. We use the well known IRIS data as an experimental data. Unsupervised clustering of IRIS data typically results in 15 - 17 clustering error. However, the proposed algorithm has only six clustering errors.

• 대한산업공학회지
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• 제28권4호
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• pp.360-367
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• 2002

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells. The purpose of this study is to develop a two-phase machine-part group formation algorithm based on Self-Organizing Maps (SOM). In phase I, it forms machine cells from the machine-part incidence matrix by means of SOM whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the input vectors. In phase II, it generates part families which are assigned to machine cells by means of machine ratio related with processing part and it gives machine-part group formation. The proposed algorithm performs remarkably well in comparison with many well-known algorithms for the machine-part group formation problems.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 1999년도 유체기계 연구개발 발표회 논문집
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.85-90
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• 1996

In this paper, the self-organizing fuzzy logic controller is developed for water level control of steam generator. In comparison with conventional fuzzy logic controllers, this controller performs control task with no control rules at initial and creates control rules as control behavior goes on, and also modifies its control structure when uncertain disturbance is suspected. Selected parameters in the fuzzy logic controller are updated on-line by the gradient descent loaming algorithm based on the performance cost function. This control algorithm is applied to water level control of steam generator model developed by Lee, et al. The computer simulation results confirm good performance of this control algorithm in all power ranges. This control algorithm can be expected to be used for automatic control of feedwater control system in the nuclear power plant with digital instrumentation and control systems.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1737-1744
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• 2004

In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LBG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codebook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks.

• 산업경영시스템학회지
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• 제23권61호
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• pp.137-146
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• 2000

One of the problems faced in implementing cellular manufacturing systems is machine-part group formation. This paper proposes machine-part grouping algorithms based on Self-Organizing Map(SOM) neural networks and K-Means algorithm in cellular manufacturing systems. Although the SOM spreads out input vectors to output vectors in the order of similarity, it does not always find the optimal solution. We rearrange the input vectors using SOM and determine the number of groups. In order to find the number of groups and grouping efficacy, we iterate K-Means algorithm changing k until we cannot obtain better solution. The results of using the proposed approach are compared to the best solutions reported in literature. The computational results show that the proposed approach provides a powerful means of solving the machine-part grouping problem. The proposed algorithm Is applied by simple calculation, so it can be for designer to change production constraints.

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.223-230
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• 2001

자기구성 지도는 주어진 입력에 대해 올바른 출력 값이 제공되지 않는 비교사 방식으로 학습된다. 또한, 반응하는 순서나 위치를 통해 위상이 보존(topology preserving)되는 특성을 가지고 있어 많은 분야에 응용되고 있다. 그러나, 자기 구성지도는 학습이 되기 전에 위상을 미리 고정시켜야 하기 때문에 실제 문제에 적용하기 어렵다는 단점을 가지고 있다. 구조 적응형 자기구성 지도는 자기구성 지도의 고정된 구조 때문에 발생하는 문제를 해결하기 위해 지도의 구조를 학습 중에 적절하게 변경시킨다. 이때, 변화된 구조의 가중치를 어떻게 초기화시킬 것인가 하는 것이 또한 중요한 문제이다. 이 논문에서는 구조 적응형 자기구성 지도 모델에서 유전자 알고리즘을 이용하여 분화된 노드의 가중치를 결정하는 방법을 제안한다. 이 방법은 기존의 구조 적응형 자기구성 지도보다 다소 높은 인식률을 보였고, 숫자 별 인식률 편차를 줄일 수 있었다. 오프라인 필기 숫자 데이터로 실험한 결과, 제안한 방법이 유용함을 알 수 있었다.

• 인터넷정보학회논문지
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• 제2권2호
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• pp.105-116
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• 2001

FA의 중요성이 증가함에 따라 AGV(Autonomous Guided Vehicle)의 역할 또한 중요시되고 있다. 본 논문은 인공지능의 여러 방법론을 통합하여 하이브리드 형태의 제어기가 가질 수 있는 상호 보완적인 특징을 이용하여 자기 조직이 가능한 유전자 알고리즘에 의한 퍼지 제어기로써 능동적이고 효과적인 AGV 제어기를 구성한다. 자기 조직이 가능한 퍼지 제어기를 구성하기 위하여 GA(Genetic Algorithm)를 사용하여 맴버쉽 함수와 제어 규칙을 최적에 근사하게 튜닝하였으며 제어 규칙의 자기 수정 또는 생성을 통하여 제어 성능을 향상시킨다.

• 전력전자학회논문지
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• 제2권4호
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• pp.45-55
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• 1997

FA의 중요성이 증가함에 따라 AGV(Autonomous Guided Vehicle)의 역할 또한 중요시되고 있다. 본 논문은 인공 지능의 여러 방법론을 통합하여 하이브리드 형태의 제어기가 가질 수 있는 상호 보완적인 특징을 이용하고자 하며, 유전자 알고리즘에 의한 자기조직이 가능한 퍼지제어기로써 능동적이고 효과적인 AGV 제어기를 구성하고자 한다. 자기 조직이 가능한 퍼지 제어기는 구성하기 위하여 GA(Genetic Algorithm)를 사용하여 멤버십 함수와 제어 규칙을 최적에 근사하게 튜닝하였으며 제어 규칙의 자기 수정 또는 생성을 통하여 제어 성능을 향상시킨다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.408-411
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• 1993

In this paper, we propose the Fuzzy Neural Controller with a Self-Organizing Map based on the fuzzy relation neuron. The fuzzy ndes expressing the input-output relation of the system are obtained by using the fuzzy relation neuron and updated automatically by means of the generalized delta rule. Also, the proposed method has a capability to express the knowledge acquired from the input-output data in form of fuzzy inferences rules. The learning algorithm of this fuzzy relation neuron is described. The effectiveness of the proposed fuzzy neural controller is illustrated by applying it to a number of test data sets.