...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Transactions of the ASABE >Genetic Algorithms (GAs) and Evolutionary Strategy to Optimize Electronic Nose Sensor Selection
【24h】

Genetic Algorithms (GAs) and Evolutionary Strategy to Optimize Electronic Nose Sensor Selection

机译：遗传算法（GA）和进化策略优化电子鼻传感器的选择

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, potentially improve classification accuracy by removing irrelevant sensors, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose was optimized for apple defect detection by selecting the most relevant of its 32 internal sensors using various selection methods. The contribution of each sensor was first evaluated statistically by calculating the F-value. By keeping only the top 90% cumulative F-values, 25 sensors were selected and the classification error rate was 25.4%. Sequential forward/backward search methods reduced the minimum classification error rate to 6.1%. Two more heuristic optimization algorithms, genetic algorithm (GA) and the covariance matrix adaptation evolutionary strategy (CMAES), were applied and compared. Although both algorithms gave a best classification error rate of 4.4%, the average classification error rate of CMAES over 30 random seed runs was 5.0% (SD = 0.006), which was better than the 5.2% (SD = 0.004) from the GA. The final optimal solution sets obtained by using an integer GA showed that including more sensors did not guarantee better classification performance. The best reduction in classification error rate was 10%, while the number of sensors was reduced by 75%. This study provided a robust and efficient optimization approach to reduce the high dimensionality of electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.
机译：电子鼻数据的高维数增加了其在分类模型中使用的难度。降低此高维度有助于减少变量数量，通过删除不相关的传感器来潜在地提高分类精度，并减少计算时间和传感器成本。在这项研究中，Cyranose 320电子鼻针对苹果缺陷检测进行了优化，方法是使用各种选择方法从32个内部传感器中选择最相关的一种。首先通过计算F值对每个传感器的贡献进行统计评估。通过仅保留最高的90％累积F值，选择了25个传感器，分类错误率为25.4％。顺序向前/向后搜索方法将最小分类错误率降低到6.1％。并比较了两种启发式优化算法：遗传算法（GA）和协方差矩阵适应进化策略（CMAES）。尽管两种算法的最佳分类错误率均为4.4％，但30次随机种子运行中CMAES的平均分类错误率为5.0％（SD = 0.006），优于GA的5.2％（SD = 0.004）。通过使用整数GA获得的最终最佳解决方案集显示，包括更多的传感器并不能保证更好的分类性能。分类错误率的最佳减少是10％，而传感器的数量减少了75％。这项研究提供了一种强大而有效的优化方法，以减少电子鼻数据的高维度，从而大大改善了苹果缺陷检测中的电子鼻性能，同时有可能降低未来特定应用的总体电子鼻成本。

著录项

  • 来源
    《Transactions of the ASABE》 |2008年第1期|p.321-330|共10页
  • 作者

    C. Li P. H. Heinemann P. M. Reed;

  • 作者单位

    The authors are Changying Li, ASABE Member Engineer, Assistant Professor, Department of Biological and Agricultural Engineering, University of Georgia, Tifton, Georgia;

    Paul H. Heinemann, ASABE Member Engineer, Professor, Department of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, Pennsylvania;

    and Patrick Reed, Assistant Professor, Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania. Corresponding author: Paul H. Heinemann, 249 Agricultural Engineering Bldg., The Pennsylvania State University, University Park, PA 16802;

    phone: 814-865-2633;

    fax: 814-863-1031;

    e-mail: hzh@psu.edu.;

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

    Apple quality and safety, Covariance matrix adaptation evolutionary strategy, Electronic nose, Genetic algorithms, Optimization, Sensor selection;

    机译：苹果质量和安全性;协方差矩阵适应进化策略;电子鼻;遗传算法;优化;传感器选择;

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