一种基于扩展交替投影神经网络的弱信号分离方法研究

A Weak-Signal Separation Algorithm Based on Extended Alternating Projection Neural Networks

1引言 交替投影神经网络(Alternating Projection NeuralNetworks,APNN)[1]是由美国Washington大学Marks Ⅱ等人提出的.该神经网络自从提出到现在,没有引起同行的太多注意,关于其应用方面的研究更是寥寥无几.

Aiming at a kind of specific situation encountered in practice, the paper proposes a weak-signal separation algorithm based on Extended Alternating Projection Neural Networks (EAPNN) by combining the time-domain features of the signal with the frequency-domain features of the signal and taking advantage of conclusions on EAPNN. Simulation results demonstrate that the algorithm is effective and that the EAPNN-based signal separation algorithm is better than the RLS-based signal separation algorithm. Although the EAPNN-based algorithm is designed for the specific situation, it is also applicable to the other situations and a basic frame of the EAPNN-based signal separation is presented. Owing to adopting neural network structure, the EAPNN-based algorithm is prone to parallel computation and VLSI design, consequently can satisfy real-time processing needs.