An RLS fuzzy adaptive filter, with application to nonlinear channel equalization

The authors have developed a nonlinear adaptive filter called the recursive least squares (RLS) fuzzy adaptive filter. The key elements of the filter are a fuzzy logic system, which is constructed from a set of if-then rules, and an RLS type of adaptive algorithm for updating the parameters in the fuzzy logic system. The most important advantage of the fuzzy adaptive filter is that linguistic information and numerical information can be combined in the filter in a uniform fashion. This is applied to nonlinear communication channel equalization problems. The simulation results show that: (1) without using any linguistic information, the RLS fuzzy adaptive filter is a well-performing nonlinear adaptive filter; (2) by incorporating some linguistic description about the channel into the fuzzy adaptive filter, the adaptation speed is greatly improved; and (3) the bit error rate of the fuzzy equalizer is very close to that of the optimal equalizer