Title: Fuzzy neural network classification design using support vector machine
Authors: Lin, CT
Yeh, CM
Hsu, CF
電控工程研究所
Institute of Electrical and Control Engineering
Keywords: fuzzy neural network;fuzzy kernel function;support vector machine
Issue Date: 2004
Abstract: 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.
URI: http://hdl.handle.net/11536/18407
ISBN: 0-7803-8251-X
Journal: 2004 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL 5, PROCEEDINGS
Begin Page: 724
End Page: 727
Appears in Collections:Conferences Paper