[1]邓晓政,叶 冰.免疫 BP 网络的机载嵌入式训练系统效能评估[J].计算机技术与发展,2019,29(12):173-177.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 031]
 DENG Xiao-zheng,YE Bing.Effectiveness Evaluation of Airborne Embedded Training System of Immune BP Networks[J].,2019,29(12):173-177.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 031]
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免疫 BP 网络的机载嵌入式训练系统效能评估()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年12期
页码:
173-177
栏目:
应用开发研究
出版日期:
2019-12-10

文章信息/Info

Title:
Effectiveness Evaluation of Airborne Embedded Training System of Immune BP Networks
文章编号:
1673-629X(2019)12-0173-05
作者:
邓晓政叶 冰
中国飞行试验研究院,陕西 西安 710089
Author(s):
DENG Xiao-zhengYE Bing
Chinese Flight Test Establishment,Xi’an 710089,China
关键词:
嵌入式训练系统效能评估BP 神经网络免疫克隆选择
Keywords:
embedded training systemeffectiveness evaluationBP neural networksimmune clonal selection
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 12. 031
摘要:
针对如何定量、精准地对机载嵌入式训练系统进行效能评估的工程问题,提出了一种基于免疫 BP 神经网络的效能评估方法。 首先将效能评估工程问题建模为一个非线性回归的数学问题;其次,根据机载嵌入式训练系统的组成结构和特点,设计了简洁、实用的效能评估指标体系;再次,综合利用了免疫克隆选择优化算法全局搜索能力强的优势以及 BP神经网络算法局部搜索能力强的优点,从而快速有效地求解神经网络的突触权值,进而得到训练好的神经网络。 最后在算法验证部分,通过四组仿真数据实验,并对比经典的 BP 神经网络算法、基于进化计算的 BP 神经网络算法,结果表明该效能评估方法在评估精度和评估稳定性方面都是较优的。
Abstract:
Aiming at the engineering problem of how to quantitatively and accurately evaluate the effectiveness of the airborne embedded training system,we propose a novel effectiveness evaluation method based on immune BP neural networks. Firstly,the problem of effectiveness evaluation engineering is modeled as a nonlinear regression problem. Secondly, according to the composition and characteristics of the airborne embedded training system, a concise and practical effectiveness evaluation index system is designed. Thirdly, the advantages of immune clonal selection optimization algorithm in global search and BP neural network algorithm in local search are comprehensively utilized,so that the weights of the neural network can be solved quickly and effectively,and the trained neural network can be obtained. Finally in the algorithm verification,through four groups of simulation experiments and the comparison with classic BP neural network and BP neural network based on evolutionary computation,it shows that this method is superior in evaluation accuracy and stability.

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更新日期/Last Update: 2019-12-10