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퍼지 클러스터링기반 신경회로망 패턴 분류기의 학습 방법 비교 분석
김은후(Eun-Hu Kim),오성권(Sung-Kwun Oh),김현기(Hyun-Ki Kim) 대한전기학회 2016 전기학회논문지 Vol.65 No.9
In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as “If” clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.
최적화된 Interval Type-2 FCM based RBFNN 구조 설계
김은후(Eun-Hu Kim),송찬석(Chan-Seok Song),오성권(Sung-Kwun Oh),김현기(Hyun-Ki Kim) 대한전기학회 2017 전기학회논문지 Vol.66 No.4
In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models’ performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.
김은후(Eun-Hu Kim),오성권(Sung-Kwun Oh) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.10
본 논문에서는 각 픽셀의 정보를 하나의 차원으로 놓고 각 픽셀단위의 퍼지 패턴 분류기를 설계하고자 한다. 각 픽셀마다 주어진 클래스의 정보를 어느 정도 포함했는지를 분석하여 전처리 기능으로써의 픽셀제거를 수행한다. 그 후 나머지 픽셀들의 독립된 퍼지 패턴 분류기를 설계하고 각 클래스에 대응하는 멤버쉽 값의 평균을 이용하여 최종 출력을 선택한다. 또한 퍼지 분류기에 사용된 픽셀들의 퍼지 엔트로피를 각 모델의 가중치로 사용함으로써 각 픽셀의 중요도를 동시에 고려하여 모델 설계를 수행한다.
방사형 기저함수 신경회로망 기반 숫자 인식 시스템의 설계
김은후(Eun-Hu Kim),김봉연(Bong-Youn Kim),오성권(Sung-Kwun Oh) 대한전기학회 2017 전기학회논문지 Vol.66 No.2
In this study, we propose a design of digits recognition system based on RBFNNs through a comparative study of pre-processing algorithms in order to recognize digits in handwritten. Histogram of Oriented Gradient(HOG) is used to get the features of digits in the proposed digits recognition system. In the pre-processing part, a dimensional reduction is executed by using Principal Component Analysis(PCA) and (2D)2PCA which are widely adopted methods in order to minimize a loss of the information during the reduction process of feature space. Also, The architecture of radial basis function neural networks consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, the connection weights are used as the extended type of polynomial expression such as constant, linear, quadratic and modified quadratic. By using MNIST handwritten digit benchmarking database, experimental results show the effectiveness and efficiency of proposed digit recognition system when compared with other studies.