A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.

We have developed an efficient recursive algorithm based on the interacting multiple model (IMM) for enhancing speech degraded by additive white noise. The clean speech is modeled by the hidden filter model (HFM). The simulation results shows that the proposed method offers performance gains relative to the previous one with slightly increased complexity.