首页> 外文期刊>Mathematical Problems in Engineering >Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model
【24h】

Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

机译:基于小波神经网络模型的具体压缩测试数据估计

获取原文
获取原文并翻译 | 示例
           

摘要

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e(1), and the root mean square error, e(2), for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.
机译:首先,在本文中提出了一种遗传算法(GA)和模拟的退火(SA)优化的模糊C-Means聚类算法(FCM),这是开发的,以允许大规模混凝土立方体样本压缩测试数据的聚类分析。然后,使用基于小波神经网络(WNN)的优化误差校正时间序列估计方法,构建了混凝土立方体样本压缩强度测试数据估计模型。将集群分析结果作为数据样本,进行了混凝土质量的短期准确估算。结果发现,样本的平均绝对百分比误差,e(1)和根均方误差,e(2)为6.03385%和3.3682kn,表明该方法具有更高的估计精度,适用于具体压缩测试数据短期质量估计。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2019年第4期|4952036.1-4952036.10|共10页
  • 作者单位

    Changan Univ Sch Construct Machinery Xian 710064 Shaanxi Peoples R China;

    Changan Univ Sch Construct Machinery Xian 710064 Shaanxi Peoples R China;

    Changan Univ Sch Construct Machinery Xian 710064 Shaanxi Peoples R China;

    Xian Univ Sci & Technol Xian 710054 Shaanxi Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号