改进SFLA-BP神经网络在遮盖干扰信号识别应用  

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

作　　者：杨洁 褚书培 YANG Jie;CHU Shupei(School of Communication and Information Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China)

机构地区：[1]西安邮电大学通信与信息工程学院,陕西西安710121

出　　处：《传感器与微系统》2020年第8期155-157,160,共4页Transducer and Microsystem Technologies

基　　金：陕西省教育厅专项基金资助项目(17JK0693)。

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。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.

关 键 词：遮盖性干扰信号 混合蛙跳算法 天牛须搜索算法 反向传播(BP)神经网络 网络模型 

分 类 号：TN911.6[电子电信—通信与信息系统]