优化回声状态网络混沌跳频码预测方法研究  

Chaotic Frequency Hopping Code Prediction Method Based on Optimized Echo State Network

作　　者：吕季杰 杨俊安[1,2] 刘辉[1,2] 

机构地区：[1]电子工程学院,安徽合肥230037 [2]安徽省电子制约技术重点实验室,安徽合肥230037

出　　处：《信号处理》2016年第5期549-557,共9页Journal of Signal Processing

基　　金：国家高技术研究发展计划(863计划)资助课题;安徽省自然科学基金项目(1308085QF99)

摘　　要：以混沌跳频码预测为背景,针对现有预测方法中存在的缺乏记忆能力导致识别准确率不高以及运算量大等问题,论文提出了基于优化回声状态网络的混沌跳频码预测方法。该方法在继承回声状态网络优良性能的同时,利用改进遗传算法优化网络储备池参数,较好地解决了参数选择问题,使其具有更强的针对性和更好的预测效果。论文以logistic-kent映射、Lorenz系统和Mackey-Glass系统跳频码为样本数据,通过改进遗传算法确定最优储备池参数并进行仿真实验,将仿真结果与其他文献结果作了比较,证明了该预测方法的优越性。In this paper,we propose a new prediction method of chaotic frequency hopping codes based on the optimized echo state network,in order to overcome the problem of low recognition accuracy and vast computation of mathematic prediction models with their incapability of memorization. This method inherits the excellent performance of ESN and the improved genetic algorithm solves the problem of dynamic reservoir parameters at the same time,making the mentod has more pertinence and better prediction performance. With optimized network parameters and sample data chosen from Logistickent mapping,Lorenz system and Mackey-Glass system frequency hopping codes,simulation experiments have been conducted. The experiments proves the validity of the proposed method in the comparison with other literatures.

关 键 词：混沌跳频码 跳频码预测 回声状态网络 改进遗传算法 

分 类 号：TN914.4[电子电信—通信与信息系统]