Fuzzy C-means based support vector machine for channel equalisation
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.
fuzzy c-means support vector machine support vectors channel equalisation fuzzy clustering