標題: Support-vector-based fuzzy neural network for pattern classification
作者: Lin, CT
Yeh, CM
Liang, SF
Chung, JF
Kumar, N
交大名義發表
生物科技學系
電控工程研究所
National Chiao Tung University
Department of Biological Science and Technology
Institute of Electrical and Control Engineering
關鍵字: fuzzy kernel function;fuzzy neural network (FNN);kernel method;mercer theorem;pattern classification;support vector machine (SVM)
公開日期: 1-Feb-2006
摘要: 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.
URI: http://dx.doi.org/10.1109/TFUZZ.2005.861604
http://hdl.handle.net/11536/12680
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2005.861604
期刊: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 14
Issue: 1
起始頁: 31
結束頁: 41
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