Presents a decision feedback equalizer (DFE) that uses type-2 fuzzy adaptive filters (FAFs). These FAFs are realized using an unnormalized type-2 TSK fuzzy logic system. We apply our DFE to a nonlinear time-varying channel, and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Our DFE vastly reduces computational complexity as compared to a transversal equalizer. Simulation results show that our type-2 FAF-based DFE performs much better than nearest neighbor classifiers or a DFE based on type-1 FAFs.
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