Application of filtering fusion for FOG based on improved RBF Neural Network

In order to improve the precision of filtering for FOG signals, many filtering algorithms have been studied. In this paper, a brief description of several traditional filtering algorithms is given, such as LMS algorithm, wavelet algorithm, wavelet packet algorithm. And a new method using fusion algorithm for FOG signals based on RBF Neural Network is proposed. However, the structure of traditional RBF neural network is very complex, in order to simplify the network, subtractive clustering algorithm is introduced. The simulation results are analyzed and compared, the comparison showed that the proposed method has a better performance in filtering than traditional methods.