基于IGWO-ELM的风洞平衡信号检测系统的温度补偿
Xiang Dong1, Hu Xu1, Huibin Cao2,3
1School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种改进的灰狼优化 (IGWO) 和极端学习机器 (ELM) 方法,以减少风洞平衡测量误差. 在不同的温度环境中,IGWO-ELM方法显著提高了准确性.
科学领域:
- 航空航天工程 航空航天工程
- 测量科学 测量科学 测量科学
- 人工智能的人工智能
背景情况:
- 风洞平衡系统对于航空航天应用至关重要.
- 环境温度变化带来了显著的测量误差.
- 现有的补偿方法在不同温度下缺乏足够的准确性.
研究的目的:
- 为风洞平衡系统开发强大的温度补偿方法.
- 为了提高测量准确性和稳定性在不同的温度条件下.
- 为了解决当前温度补偿技术的局限性.
主要方法:
- 一个改进的灰狼优化 (IGWO) 算法是通过改进初始群体,收因子和代权重来开发的.
- IGWO算法优化了极端学习机器 (ELM) 的温度补偿参数.
- 为了验证该方法,进行了校准脱和高低温实验.
主要成果:
- 拟议的IGWO-ELM方法显示了测量误差的显著降低,从20%FS降至0.04%FS.
- 进行比较的实验表明,IGWO-ELM的性能优于标准ELM,GWO-ELM,PSO-ELM和GWO-RBFNN.
- 改进后的系统表现出优越的温度补偿效应.
结论:
- IGWO-ELM 方法有效地弥补了风洞平衡系统中因温度而产生的错误.
- 这种方法显著提高了气动力学力和动量测量的准确性和稳定性.
- 该研究提供了一个可靠的解决方案,用于在可变的环境温度下精确的风洞测试.
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