Integrated Adaptive Fuzzy Clustering Research Articles

선택적 학습률을 활용한 학습법칙을 사용한 신경회로망

본 논문은 연결강도를 조정할 때 결정 경계선 근처에 있는 데이터를 더 반영하는 학습법칙을 제안하였다. 이 학습법칙은 outlier가 결정 경계선에 미치는 영향을 줄여 더 나은 결정 경계선을 형성하도록 한다. 제안하는 학습법칙을 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망의 구조에 적용하였다. IAFC 신경회로망은 배운 것을 유지하는 안정성이 있으면서, 새로운 것을 배울 수 있는 안정성이 있다. 이 퍼지 신경회로망의 성능과 LVQ(Learning Vector Quantization) 신경회로망 및 오류역전파 신경회로망의 성능과 비교하였다. 실험결과 제안하는 퍼지 신경회로망의 성능이 우수함을 보여주었다. 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.

개선된 IAFC 퍼지 신경회로망을 이용한 차량 번호판 인식

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.

데이터와 클러스터들의 대표값들 사이의 거리를 이용한 퍼지학습법칙

학습법칙은 신경회로망의 성능에 중요한 영향을 미친다. 본 논문은 데이터와 클래스들의 대표값들 사이의 거리를 고려하여 학습률을 정하는 새로운 퍼지 학습법칙을 제안한다. 클래스들의 대표값을 조정할 때, 이러한 고려는 outlier에 비하여 결정경계선 근처에 있는 데이터의 반영도를 높임으로써 outlier의 클래스의 대표값에 미치는 영향도를 낮출 수 있다. 따라서 outlier들이 결정경계선을 악화시키는 것을 방지할 수 있다. 이 새로운 퍼지 학습법칙을 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 제안한 퍼지 신경회로망과 다른 감독 신경회로망들의 성능을 비교하기 위하여 iris 데이터를 사용하였다. iris 데이터를 사용하여 테스트한 결과 제안한 퍼지 신경회로망의 성능이 우수함을 보였다. 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.

클래스간의 거리를 고려한 학습법칙을 사용한 퍼지 신경회로망 모델

본 논문은 입력 벡터와 클래스들의 대표값들간의 유클리디안 거리들을 사용한 새로운 퍼지 학습법칙을 제안한다. 이 새로운 퍼지 학습을 supervised IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망 4에 적용하였다. 이 신경회로망은 안정성을 유지하면서도 유연성을 가지고 있다. iris 데이터를 사용하여 테스트한 결과 supervised IAFC 신경회로망 4는 오류역전파 신경회로망과 LVQ 알고리듬보다 성능이 우수하였다. 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.

비대칭 퍼지 학습률을 이용한 퍼지 신경회로망 모델

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.

An adaptive integrated fuzzy clustering model for pattern recognition

Abstract The extension of neural net based crisp clustering algorithms to fuzzy clustering algorithms has been addressed by many researchers in recent years. However, such neuro-fuzzy clustering algorithms developed so far suffer from restrictions in identifying the actual decision boundaries among clusters with overlapping regions. These restrictions are induced by the choice of the similarity measure and representation of the clusters. An integrated adaptive fuzzy clustering (IAFC) algorithm is presented to generate improved decision boundaries by introducing a new similarity measure and by integrating the advantages of the fuzzy optimization constraint of fuzzy c-means (FCM), the control structure of adaptive resonance theory (ART-1), and a fuzzified Kohonen-type learning rule. The effect of the new similarity measure in finding nonlinear decision boundaries among closely located cluster centroids is demonstrated with computer generated data. We use the IRIS data set and a subset of the tethered satellite system simulation data set to compare the convergence rate and misclassifications resulting from IAFC algorithm with other clustering algorithms.