A method of designing nonlinear channel equalizer using conditional fuzzy c-means clustering

We propose a new method of designing a nonlinear channel equalizer using an adaptive neuro-fuzzy clustering method called a conditional fuzzy c-means. The structure identification of an adaptive neuro-fuzzy system is performed by the conditional fuzzy c-means clustering method with the homogeneous properties of the given input and output data. The parameter identification is established by hybrid learning using the back-propagation algorithm and recursive least squares estimation. Experimental results demonstrate that the proposed method improves the performance of the neuro-fuzzy system. Finally. we apply the proposed method to designing a nonlinear channel equalizer and obtain better results than previous methods.