基于蛇算法优化的改进RBF神经网络的航天电磁继电器贮存寿命预测方法
Storage Life Prediction Method of Aerospace Electromagnetic Relay with Improved RBF Neural Network Based on Snake Algorithm Optimization
李久鑫 1王召斌 1朱佳淼1
作者信息
- 1. 江苏科技大学 自动化学院,江苏镇江 212003
- 折叠
摘要
针对航天电磁继电器的接触电阻预测和预测精度问题,提出了一种基于蛇优化(SO)算法改进BRF神经网络的模型.在传统径向基函数(RBF)模型基础上,通过SO算法对其权值参数进行优化,从而更好地预测继电器接触电阻值.基于S0-RBF模型与RBF模型、GA-RBF模型分别预测接触电阻,对比分析预测结果,表明所提模型具有较高的预测精度.
Abstract
Aiming at the prediction and prediction accuracy of contact resistance of aerospace electromagnetic relays,a radial basis function(BRF)neural network model based on snake optimization(SO)algorithm is proposed.On the basis of the traditional RBF model,the SO algorithm is used to optimize the weight parameters so as to better predict the contact resistance value of the relay.The constructed SO-RBF prediction model is compared with RBF model.The models are used to predict the change trend of contact resistance.The comparison and analysis of the prediction results show that the proposed model has high prediction accuracy.
关键词
RBF神经网络/退化试验/贮存/继电器Key words
radial basis function(RBF)neural network/degradation test/storage/relay引用本文复制引用
基金项目
国家自然科学基金(51507074)
江苏省研究生科研与实践创新计划(KYCX23_3875)
出版年
2024
国家科技期刊平台
NSTL
万方数据