A non-linear noise canceller based on additive-multiplicative fuzzy neural networks
Donghai Zhai, Li Li, Fan Jin · 2003
A non-linear noise canceller based on the additive-multiplicative fuzzy neural network (AMFNN) is proposed in this paper. The novel noise canceller has the advantages of neural networks, such as parallel-distributed information processing, fault-tolerance and robustness. In this paper, AMFNN is used to approximate noise, and the noise approximated is cancelled from the measuring signal to obtain useful signal. Here, the AMFNN model, the learning algorithm and the universal approximation are discussed. The simulation results show that this method has a strong noise canceling capability.