计算机与现代化 ›› 2022, Vol. 0 ›› Issue (12): 42-49.

• 人工智能 • 上一篇    下一篇

基于粒子群优化BP神经网络的可靠性评估模型

  

  1. (南京航空航天大学计算机科学与技术学院,江苏南京211106)
  • 出版日期:2023-01-04 发布日期:2023-01-04
  • 作者简介:王颖颖(1996—),女,河南商丘人,硕士研究生,研究方向:软件工程,可靠性,E-mail: 876014868@qq.com; 通信作者:庄毅(1956—),女,教授,博士生导师,研究方向:分布计算,可靠性,E-mail: zy16@nuaa.edu.cn; 孙逸帆(1988—),男,硕士研究生,研究方向:星载计算机架构,抗辐射加固。
  • 基金资助:
    国家自然科学基金资助项目(61572253); 航空科学基金资助项目(2016ZC52030, 20185152035, 20150652008)

Reliability Evaluation Model of BP Neural Network Based on Particle Swarm Optimization

  1. (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
  • Online:2023-01-04 Published:2023-01-04

摘要: CPU的可靠性对计算机系统至关重要。针对神经网络等方法在可靠性分析与评估中参数优化困难、模型评估精度不够准确等问题,提出一种基于粒子群优化BP神经网络的可靠性评估模型。该模型利用由正弦映射优化的PSO算法对BP神经网络的权值和阈值进行优化,提高BP神经网络的收敛速度以及评估精度。基于CPU中各功能模块的可靠度,根据改进的BP神经网络模型建立CPU的可靠性评估模型,通过模型训练与测试完成对CPU的可靠性评估。通过对比实验,验证该模型对辐射环境下CPU可靠性评估的有效性和准确性。

关键词: CPU, 可靠性评估, 粒子群优化算法, BP神经网络, 软错误

Abstract: The reliability of the CPU is critical to a computer system. For the problem that the difficulty of parameter optimization and inaccurate evaluation accuracy in reliability analysis and evaluation of methods such as neural network, this paper proposes a reliability evaluation model based on particle swarm optimization BP neural network. The model optimized the PSO algorithm optimized by the sine map, and then optimized the weights and thresholds of the BP neural network. Through this method, the weights and thresholds of the BP neural network were optimized. Based on the reliability of each functional module in the CPU, a reliability evaluation model of the CPU was established according to the improved BP neural network model. The reliability evaluation of the CPU was completed through model training and testing. Through comparative experiments, the validity and accuracy of the model for CPU reliability evaluation under radiation environment are verified.

Key words: CPU, reliability evaluation, PSO Algorithm, BP neural network; , soft errors