• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.153-157
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• 2004

본 논문은 패턴인식을 위해 사용할 수 있는 감독학습을 이용한 supervised IAFC neural network 1과 supervised IAFC neural network 2를 제안하였다 Supervised IAFC neural network 1과 supervised IAFC neural network 2는 LVQ(Learning Vector Quantization)를 퍼지화한 새로운 퍼지 학습법칙을 사용하고 있다. 이 새로운 퍼지 학습 법칙은 기존의 학습률 대신에 퍼지화된 학습률을 사용하고 있는데, 이 퍼지화된 학습률은 조건 확률을 퍼지화 한 것에 근간을 두고 있다. Supervised IAFC neural network 1과 supervised IAFC neural network 2의 성능과 오류역전파 신경회로망의 성능을 비교하기 위하여 iris 데이터를 사용하였는데, 실험결과 supervised IAFC neural network 2 의 성능이 오류역전파 신경회로망의 성능보다 우수함이 입증되었다.

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

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.460-465
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• 2006

This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.6-12
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• 2009

In this paper, we propose a system that extracts licence plate and recognizes numerals in the licence plate. The candidate area of licence plate is extracted using the improved IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. And the morphological filters are used to reduce noise from the extracted licence plate. The extracted licence plate is standardized using Hough transform and geometric transform. Backpropagation neural network is used to recognize numerals that are separated using the projection technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.672-676
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• 2010

This paper presents a learning rule that weights more on data near decision boundary. This learning rule generates better decision boundary by reducing the effect of outliers on the decision boundary. The proposed learning rule is integrated into IAFC neural network. IAFC neural network is stable to maintain previous learning results and is plastic to learn new data. The performance of the proposed fuzzy neural network is compared with performances of LVQ neural network and backpropagation neural network. The results show that the performance of the proposed fuzzy neural network is better than those of LVQ neural network and backpropagation neural network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.109-112
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• 2006

본 논문은 클래스들의 대표값들과 입력 벡터와의 거리를 사용한 새로운 퍼지 학습법칙을 제안한다. 이 새로운 퍼지 학습을 supervised IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 이 새로운 신경회로망은 안정성을 유지하면서도 유연성을 가지고 있다. iris 데이터를 사용하여 테스트한 결과 supervised IAFC 신경회로망 4는 오류 역전파 신경회로망과 LVQ 알고리즘보다 성능이 우수하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.431-435
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• 2013

Aroma has been used for healing for a long time. The healing effects depend on aroma used. We made gas sensor array system to classify aromas systematically. We used outputs of sensors as the input to IAFC neural network. Results show that the neural network successfully classified jasmine, orange, roman chamomile, and lavender into 4 classes, and classified without any error.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.186-189
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• 2005

본 논문에서는 LVQ를 퍼지화한 새로운 퍼지 학습 법칙들을 제안하였다. 퍼지 LVQ 학습법칙 1은 기존의 학습률 대신에 퍼지 학습률을 사용하였는데 이는 조건 확률의 퍼지화에 기반을 두고 있다. 퍼지 LVQ 학습법칙 2는 클래스들 사이에 존재하는 입력벡터가 결정 경계선에 대한 정보를 더 가지고 있는 것을 반영한 것이다. 이 새로운 퍼지 학습 법칙들을 improved IAFC(Integrted Adaptive Fuzzy Clustering)신경회로망에 적용하였다. improved IAFC신경회로망은 ART-1 (Adaptive Resonance Theory)신경회로망과 Kohonen의 Self-Organizing Feature Map의 장점을 취합한 퍼지 신경회로망이다. 제안한 supervised IAFC 신경회로망 1과 supervised IAFC neural 신경회로망 2의 성능을 오류 역전파 신경회로망의 성능과 비교하기 위하여 iris 데이터를 사용하였는데 Supervised IAFC neural network 2가 오류 역전파 신경회로망보다 성능이 우수함을 보여주었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.226-231
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• 2007

This paper presents a method of colored object extraction from an image using the fuzzy neural network. Fuzzy neural network divides an image into two clusters. It extracts the prototypes of Cb and Cr of object and background by controlling the vigilance parameter. The proposed method extracted object regardless of the position, the size, and the intensity of object. We compared the performance of the proposed method with that of the method of using subjective threshold value. And, we compared the performance of the proposed method with that of the method of using subjective threshold value by using several images with added noises.