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.