基于交替投影神经网络的带限信号外推算法

本文通过对频带受限数字信号的离散傅立叶变换特性的研究,引进了交替投影神经网络,并将其应用范围从实数域拓广到复数域,且给出了在复数域仍然成立的若干结论.运用这些结论,在对网络噪声抑制、网络收敛速度及待外推信号因截断而造成频谱严重外泄等问题的分析与讨论的基础上,提出了一种基于交替投影神经网络的外推算法.仿真实验表明该方法是行之有效的.另外,该算法对频谱外推同样适用;由于它采用全互连神经网络结构,易于并行计算和VLSI实现,从而可满足军事上实时处理的需要.

An Extrapolation Algorithm for Band-Limited Signals Based on Alternating Projection Neural Networks

By studying the discrete Fourier transform properties of the band limited digital signal,the authors introduce alternating projection neural networks into the paper,expand APNN's application scope from real field to complex field,and present several important conclusions on APNN.Analyzing and discussing network's tolerance to noise,convergence rate and the spectral leakage problem of the truncated signal expected to be extrapolated by using these conclusions,the paper presents an extrapolation algorithm for band limited signals based on alternating projection neural networks.A lot of simulation experiments show that the algorithm is effective.In addition,the algorithm is also effective to spectrum extrapolation.Owing to adopting network structure,the algorithm is prone to parallel computation and VLSI design,and consequently can satisfy real time military processing needs.