188宝金博页面版

  • 图案背景
  • 纯色背景
视图
标记
批注
批注本地保存成功，开通会员云端永久保存 去开通
yanbubu

上传于：2018-04-15

粉丝量：20

该文档贡献者很忙，什么也没留下。


  • 相关
  • 目录
  • 笔记
  • 书签

188宝金博页面版:更多相关文档

  • 基于主成分分析的BP神经网络在形变预测中的应用

    星级： 5 页

  • 基于主成分分析的BP 神经网络在形变预测中的应用

    星级： 5 页

  • 基于主成分分析的BP神经网络及其在需水预测中的应用

    星级： 5 页

  • 基于主成分分析的BP神经网络在药品销售预测中的应用

    星级： 3 页

  • 主成分分析—BP神经网络在备件需求预测中的应用

    星级： 4 页

  • BP神经网络主成分分析在试飞数据预测中的应用

    星级： 2 页

  • 主成分分析-bp神经网络在备件需求预测中的应用

    星级： 5 页

  • [精品]基于主成分分析的BP神经网络在药品销售预测中的应用

    星级： 3 页

  • 基于主成分分析的BP神经网络在岩性识别中的应用

    星级： 4 页

  • 基于主成分分析和bp神经网络的股价预测

    星级： 36 页

暂无目录

点击鼠标右键菜单,创建目录

    暂无笔记

    选择文本，点击鼠标右键菜单，添加笔记

    暂无书签

    在左侧文档中，点击鼠标右键，添加书签

    188宝金博页面版: 基于主成分分析的BP神经网络在水华预测中的应用

    下载积分： 1000

    内容提示： International Journal of Ecology 世界生态学, 2018, 7(2), 53-60 Published Online May 2018 in Hans. http://www.hanspub.org/journal/ije https://doi.org/10.12677/ije.2018.72008 文章引用: 夏杰, 吴文青, 许海洋. 基于主成分分析的 BP 神经网络在水华预测中的应用[J]. 世界生态学, 2018, 7(2): 53-60. DOI: 10.12677/ije.2018.72008 Application of BP Neural Network Based on Principal Component Analysis in Algal Bloom Prediction Jie Xia, Wenqing Wu, Haiyang Xu School of Science, Southwest University of Science and Techno...

    文档格式：PDF | 页数：8 | 浏览次数：25 | 上传日期：2018-04-15 16:45:00 | 文档星级：
    International Journal of Ecology 世界生态学, 2018, 7(2), 53-60 Published Online May 2018 in Hans. http://www.hanspub.org/journal/ije https://doi.org/10.12677/ije.2018.72008 文章引用: 夏杰, 吴文青, 许海洋. 基于主成分分析的 BP 神经网络在水华预测中的应用[J]. 世界生态学, 2018, 7(2): 53-60. DOI: 10.12677/ije.2018.72008 Application of BP Neural Network Based on Principal Component Analysis in Algal Bloom Prediction Jie Xia, Wenqing Wu, Haiyang Xu School of Science, Southwest University of Science and Technology, Mianyang Sichuan Received: Mar. 18 th , 2018; accepted: Apr. 2 nd , 2018; published: Apr. 9 th , 2018 Abstract With the intensification of water pollution and eutrophication of freshwater ecosystems, large areas of algal bloom have been included, which not only destroy the ecosystems, but also cause huge economic losses. Therefore, it is very important to predict the occurrence of algal bloom ac-cording to the physical and chemical factors of water body. Firstly, according to the data of the pond for 1~15 weeks, the main influencing factors of 13 physical and chemical factors affecting the total plankton were analyzed based on principal component analysis (PCA). The main influencing factors of algal blooms were total nitrogen, transparency, dissolved oxygen, ammonium nitrogen, salinity, total phosphorus and dissolved oxygen. Secondly, according to the main seven physical and chemical factors identified as the input layer of BP neural network, the plankton biomass was used as the output layer to predict the occurrence of algal bloom. The results show that the fitting coefficient between the predicted result and the true value of the BP neural network model based on principal component analysis is as high as 0.9912. Therefore, the research method in this paper can effectively predict the occurrence of algal bloom. Keywords Algal Bloom Prediction, Physical and Chemical Factors, Principal Component Analysis, BP Neural Network 基于主成分分析的BP 神经网络在水华预测中的应用 夏 夏 杰，吴文青，许海洋 西南科技大学理学院，四川 绵阳

    阅读了该文档的用户还阅读了这些文档

    188宝金博页面版:关于我们

    188宝金博页面版:帮助中心

    188宝金博页面版:关注我们

    • 新浪微博

    关注188宝金博页面版公众号

    188宝金博页面版
    阅读
    APP
    阅读
    返回
    顶部
    188宝金博页面版官网登录在线平台入口（2026已更新）—江苏协昌电子科技股份有限公司