家电科技 ›› 2023, Vol. 0 ›› Issue (1): 114-117.doi: 10.19784/j.cnki.issn1672-0172.2023.01.020

• 论文 • 上一篇    下一篇

基于BP神经网络预测某空气净化器环境电压的研究

熊明洲, 王成成, 石磊, 陈丹慧   

  1. 珠海格力电器股份有限公司 广东珠海 519070
  • 出版日期:2023-02-01 发布日期:2023-04-24
  • 通讯作者: 石磊,E-mail:huozhe691@163.com。
  • 作者简介:熊明洲,硕士学位。研究方向:数据分析及应用。地址:广东省珠海市香洲区前山金鸡西路789号。E-mail:244956081@qq.com。

Prediction of ambient voltage of an air purifier based on BP neural network

XIONG Mingzhou, WANG Chengcheng, SHI Lei, CHEN Danhui   

  1. Gree Electric Appliances, Inc. of Zhuhai Zhuhai 519070
  • Online:2023-02-01 Published:2023-04-24

摘要: 为实现某空气净化器环境电压的智能检测,运用sigmoid激活函数,构建了一种基于温升曲线预测环境电压的BP神经网络模型。结果显示,当输入层神经元数为27~53个,隐含层神经元数为25个时,对环境电压的预测能力较好。训练集的环境电压的实际值与预测值的相关系数为0.996,模型误差范围为-2.24%~5.26%,评估新数据的相对误差较小,具有良好的预测性,研究结果显示电压识别模型可用于智能预测环境电压。

关键词: 空气净化器, 神经网络, 机器学习, 电压, 预测

Abstract: In order to achieve intelligent detection of the ambient voltage of an air purifier, a BP neural network model based on the temperature rise curve to predict the ambient voltage is constructed by using the sigmoid activation function. The results showed that when there were 27~53 neurons in the input layer and 25 neurons in the implicit layer, the prediction ability of the ambient voltage was better. The correlation coefficient between the actual value of the environmental voltage and the predicted value of the training set is 0.996, the model error range is -2.24%~5.26%, the relative error of the evaluation of the new data is small, and it has good predictability, and the results show that the voltage identification model can be used to intelligently predict the ambient voltage.

Key words: Air cleaner, Neural network, Machine learning, Voltage, Forecast

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