Echo Cancellation in Cellphone Using Neurofuzzy Filter

Abstract

Computational intelligence combines neural network, fuzzy systems and evolutionary computing. In this manuscript a neurofuzzy filter (NFF) is presented, which is based on fuzzy if-then rules (structure learning) and the tuning of the parameters of membership function (parameter learning). In the structure learning, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning, the consequent parameters are tuned optimally by either least mean square (LMS) or recursive least squares (RLS) algorithms and the pre condition parameters are tuned by backpropagation algorithm. Both the structure and parameter learning are performed simultaneously as the adaptation proceeds. Simulation for echo cancellation in cellphone is performed. Good performance is achieved by applying the NFF to echo cancellation on a cellphone.