基于IDBO-SVM的造纸车间温湿度预测方法研究
Research on Temperature and Humidity Prediction Method in Papermaking Workshop Based on IDBO-SVM
张育豪 1马添翼 1李婷 1刘子宸 1孙晓龙1
作者信息
- 1. 北京印刷学院机电工程学院,北京 102600
- 折叠
摘要
造纸车间温湿度的调控是造纸过程中一个至关重要的环节.为了实现造纸车间的温湿度精准预测,综合考虑造纸行业对车间恒温恒湿的要求,本文提出了一种基于多策略改进蜣螂优化算法(Improved Dung Beetle Optimizer,IDBO)优化支持向量机(Support Vector Machine,SVM)的造纸车间温湿度预测模型.首先,通过对采集系统获取的温湿度数据进行数据插值和归一化处理,以确保数据在训练过程中能够更好地融入模型.其次,将蜣螂优化算法进行多策略改进,并用其优化SVM的核函数g与惩罚参数C.最后利用训练好的模型实现对造纸车间温湿度的预测.实验分析证明,本文所提出的模型具有较高的预测性能,温度的MAE和RMSE分别为1.176和1.465,湿度的MAE和RMSE分别为1.193和1.636.
Abstract
The control of temperature and humidity in papermaking workshops is a crucial aspect of the papermaking process. In order to achieve accurate prediction of temperature and humidity in papermaking workshops,taking into account the industry's need for consistent temperature and humidity in workshops,this study proposes a papermaking workshop temperature and humidity prediction model based on the Improved Dung Beetle Optimizer (IDBO) optimizing Support Vector Machine (SVM) with multiple strategies. Firstly,temperature and humidity data collected by the acquisition system are interpolated and normalized to ensure better integration of data into the model during training. Secondly,the Dung Beetle Optimizer algorithm is improved with multiple strategies to optimize the SVM's kernel function g and penalty parameter C. Finally,the trained model is used to predict temperature and humidity in papermaking workshops. Experimental analysis demonstrates that the proposed model exhibits high predictive performance,with MAE and RMSE for temperature being 1.176 and 1.465 respectively,and for humidity being 1.193 and 1.636 respectively.
关键词
造纸车间/温湿度预测/蜣螂优化算法/支持向量机Key words
papermaking workshop/temperature and humidity prediction/Dung Beetle Optimizer/Support Vector Machine引用本文复制引用
出版年
2024
NETL
NSTL
万方数据