Support-Vector-Based Fuzzy Neural Networks

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Provided by: International Research Publication House (IRPH)
Topic: Networking
Format: PDF
In this paper, novel Fuzzy Neural Networks (FNNs) combining with support vector learning mechanism called Support-Vector-based Fuzzy Neural Networks (SVFNNs) are proposed for pattern classification and function approximation. The SVFNNs combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning 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 SVFNNs and train the parameters.
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