Application of modified SFLA-BP neural network in covering jamming signals identification

YANG Jie; CHU Shupei

doi:10.13873/J.1000-9787(2020)08-0155-03

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Application of modified SFLA-BP neural network in covering jamming signals identification

更新时间：2025-09-11

- Application of modified SFLA-BP neural network in covering jamming signals identification
- Vol. 39, Issue 8, Pages: 155-157+160(2020)
- 作者机构：
  
  西安邮电大学通信与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)08-0155-03
  CLC： TP183;TN957.51
- Published：2020
- 稿件说明：
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[1]杨洁,褚书培.改进SFLA-BP神经网络在遮盖干扰信号识别应用[J].传感器与微系统,2020,39(08):155-157+160.

YANG Jie, CHU Shupei. Application of modified SFLA-BP neural network in covering jamming signals identification[J]. 2020, 39(8): 155-157+160.
[1]杨洁,褚书培.改进SFLA-BP神经网络在遮盖干扰信号识别应用[J].传感器与微系统,2020,39(08):155-157+160. DOI： 10.13873/J.1000-9787(2020)08-0155-03.

YANG Jie, CHU Shupei. Application of modified SFLA-BP neural network in covering jamming signals identification[J]. 2020, 39(8): 155-157+160. DOI： 10.13873/J.1000-9787(2020)08-0155-03.

摘要

提出一种改进混合蛙跳算法(SFLA)

采用天牛须搜索(BAS)算法来优化混合蛙跳算法的子种群的局部搜索能力

以适应度函数作为评价标准

通过不断对最差个体进行更新来提高子种群的整体水平

优化出整个种群的最优解

即神经网络最佳的初始权值、阈值

对反向传播(BP)神经网络进行训练

得出最优的网络模型

使用BP神经网络、SFLA-BP神经网络和改进SFLA-BP神经网络将雷达有源遮盖性干扰信号分类处理。实验结果表明:改进SFLA-BP神经网络对干扰信号的平均正确识别率为0.91

均优于BP神经网络的正确识别率0.853 5以及SFLA-BP神经网络的正确识别率0.891 7。

Abstract

An improved shuffled frog leaping algorithm（ SFLA） is proposed. Beetle antennae search（ BAS）algorithm is used to optimize the search ability of the sub-population of the SFLA. Using the fitness function as the evaluation criteria

by constantly updating the worst individuals to improve the overall level of the subpopulation

so as to optimize the optimal solution of the entire population

that is

the optimal initial weight and threshold of the back propagation（ BP） neural network

and train the BP neural network to obtain the optimal network model. Using the BP neural network

SFLA-BP neural network and modified SFLA-BP neural network to classify the radar active covering jamming signals. The experimental results show that the average correct recognition rate of 0. 91 of modified SFLA-BP neural network is prior to that of BP neural network of 0. 853 5 and SFLA-BP neural network of 0. 891 7.

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