Browsing by Author "Yeh, CM"

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Fuzzy neural network classification design using support vector machine
(2004)
Lin, CT
;
Yeh, CM
;
Hsu, CF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the Iris, Vehicle and Dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions.
Fuzzy neural network design using support vector regression for function approximation with outliers
(2005)
Lin, CT
;
Liang, SF
;
Yeh, CM
;
Fan, KW
;
電控工程研究所
A fuzzy neural network based on support vector learning mechanism for function approximation is proposed in this paper. Support vector regression (SVR) is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory. SVR has been shown to have robust properties against noise. A novel support-vector-regression based fuzzy neural network (SVRFNN) by integrating SVR technology into FNN is developed. The SVRFNN combines the high accuracy and robustness of support vector regression (SVR) and the efficient human-like reasoning of FNN for function approximation. Experimental results show that the proposed SVFNN for function approximation can achieve good approximation performance with drastically reduced number of fuzzy kernel functions.
Support-vector-based fuzzy neural network for pattern classification
(2006-02-01)
Lin, CT
;
Yeh, CM
;
Liang, SF
;
Chung, JF
;
Kumar, N
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijenn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.