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|標題:||Fuzzy C-means based support vector machine for channel equalisation||作者:||Juang, C.F.
|關鍵字:||fuzzy clustering;fuzzy C-means;support vector machine;support;vectors;channel equalisation;classification||Project:||International Journal of General Systems||期刊/報告no：:||International Journal of General Systems, Volume 38, Issue 3, Page(s) 273-289.||摘要:||
This paper proposes a new classification network, the fuzzy C-means based support vector machine (FCM-SVM) and applies it to channel equalisation. In contrast to a kernel-based SVM, the FCM-SVM has a smaller number of parameters while retaining the SVM's good generalisation ability. In FCM-SVM, input training data is clustered by FCM. The output of FCM-SVM is a weighted sum of the degrees where each input data belongs to the clusters. To achieve high generalisation ability, FCM-SVM weights are learned through linear kernel based SVM. Computer simulations illustrate the performance of the suggested network, where the FCM-SVM is used as a channel equaliser. Simulations with white Gaussian and coloured Gaussian noise are performed. This paper also compares simulation results from the FCM-SVM, the Gaussian kernel based SVM and the optimal equaliser.
|Appears in Collections:||電機工程學系所|
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