摘要
由于多径效应和频散效应导致水声信道中声信号衰减和失真严重,传统均衡技术不能满足在水声信道中应用的要求,近年来神经网络在均衡技术方面的突出表现受到广泛关注,因此,本文提出一种高效的神经网络训练算法,即基于非线性自回归神经网络的改进共生生物搜索算法(简称NARX-nSOS算法)实现水声信道均衡。该算法在非线性自回归神经网络(Nonlinear Autoregressive Neural Network with Exogenous Inputs,NARX)均衡器的基础上,用共生生物搜索算法(Symbiotic Organisms Search,SOS)来进行优化,并结合反向学习算法(Opposition-Based Learning,OBL)来提高该算法的收敛能力,利用计算机对NARX-nSOS算法的有效性进行了仿真验证,结果证明NARXnSOS算法加快了收敛速度,通信质量得到了显著提高。
Due to serious attenuation and distortion of acoustic signal caused by multipath effect and frequency dispersion effect in underwater acoustic channels,the traditional equalization techniques cannot meet the application requirements in underwater acoustic channel.Outstanding performance of neural network in equalization techniques has attracted widespread attention in recent year,so an efficient neural network training algorithm,known as the improved symbiotic organisms search algorithm based on nonlinear autoregressive neural network with exogenous inputs(NARX-nSOS),is proposed for underwater acoustic channel equalization in this paper.The algorithm is optimized with the symbiotic organisms search(SOS)algorithm based on nonlinear autoregressive neural network with exogenous inputs(NARX)equalizer and its convergence capability is improved by combining opposition-based learning(OBL)method.The effectiveness of the NARX-nSOS is verified by computer simulation,and the results demonstrate that the NARX-nSOS algorithm can accelerate convergence speed and improve communication quality significantly.
作者
王亦凡
朱婷婷
WANG Yifan;ZHU Tingting(School of Electronic Information Engineering,Xi'an Technological University,Xi'an 710021,Shaanxi,China)
出处
《声学技术》
CSCD
北大核心
2023年第2期168-173,共6页
Technical Acoustics
关键词
神经网络
信道均衡
共生生物搜索算法
反向学习
neural network
channel equalization
symbiotic organisms search algorithm
opposition-based learning
