电器与能效管理技术Issue(3)：30-35,6.DOI:10.16628/j.cnki.2095-8188.2024.03.005
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
摘要
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分类
信息技术与安全科学引用本文复制引用
李久鑫,王召斌,朱佳淼..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[J].电器与能效管理技术,2024,(3):30-35,6.基金项目
国家自然科学基金项目(51507074) （51507074）
江苏省研究生科研与实践创新计划资助项目(KYCX23_3875) （KYCX23_3875）
