文章摘要
王亦凡,朱婷婷.共生生物搜索算法在水声信道均衡中的应用[J].声学技术,2023,42(2):168~173
共生生物搜索算法在水声信道均衡中的应用
Application of symbiotic organisms search algorithm in underwater acoustic channel equalization
投稿时间:2021-12-10  修订日期:2022-02-28
DOI:10.16300/j.cnki.1000-3630.2023.02.007
中文关键词: 神经网络  信道均衡  共生生物搜索算法  反向学习
英文关键词: neural network  channel equalization  symbiotic organisms search algorithm  opposition-based learning
基金项目:
作者单位E-mail
王亦凡 西安工业大学电子信息工程学院, 陕西西安 710021 1165553213@qq.com 
朱婷婷 西安工业大学电子信息工程学院, 陕西西安 710021  
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中文摘要:
      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。
英文摘要:
      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.
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