基于支持向量机集成的船舶舱室温湿度预测
Prediction of temperature and humidity of ship cabin based on ensemble of SVM
刘丙杰 1侯慕馨 1冀海燕1
作者信息
- 1. 海军潜艇学院,山东 青岛 266119
- 折叠
摘要
针对船舶舱室温湿度保持困难、数据难以预测的问题,提出了基于克隆选择算法的支持向量机集成方法.首先,利用克隆选择算法优化个体支持向量机,根据个体预测误差进行自适应集成;然后,对舱室温湿度时间序列数据样本化,采用支持向量机集成进行训练、测试;最后通过统计测试结果以及与BP 神经网络、单支持向量机、GM(2,1)模型的预测误差对比发现,支持向量机集成模型可有效预测空调故障条件下船舶舱室温湿度的变化规律,为装备的使用和维护提供技术支持.
Abstract
For challenges in maintaining cabin temperature and humidity,as well as data predicting,ensemble of support vector machine(ESVM)based on clonal selection algorithm(CSA)was proposed.Firstly,individual SVMs were optimized by CSA,and then the cabin temperature and humidity time data series were sampled.Lastly,ESVM was used for training and testing.finally,Statistical testing results and comparison of prediction errors with BP neural network,individual SVM and GM(2,1)models show that the ESVM model can effectively predict the changes in humidity and temperature in submarine cabins under air conditioning fault conditions.The method provides a technical support for the use and maintenance of equipment.
关键词
支持向量机集成/船舶舱室/温湿度预测Key words
ensemble of SVM/ship cabin/prediction of temperature and humidity引用本文复制引用
基金项目
国家社会科学基金(2021-SKJJ-B-011)
出版年
2024
NETL
NSTL
万方数据