计算技术与自动化2025，Vol.44Issue(3)：128-132,140,6.DOI:10.16339/j.cnki.jsjsyzdh.202503023
基于IAGA-BP神经网络改进的降水测量
Improved Precipitation Measurement Based on IAGA-BP Neural Network
蔡辉 1李寅 1唐乃乔1
作者信息
- 1. 南京信息工程大学自动化学院,江苏南京 210044
- 折叠
摘要
Abstract
In order to meet the demand of miniaturized and accurate measurement of precipitation,this study uses multi-component piezoelectric ceramic pieces to collect precipitation signal as the input of neural network model,and improves the AGA-BP neural network algorithm.Firstly,the rain gauge and the standard rain barrel were used to collect precipitation da-ta at the same time to obtain the data samples required for later modeling,and then GA-BP neural network was used to mod-el the data samples,and adaptive genetic algorithm was incorporated to optimize the model,and the weak selection process was reduced due to the individual fitness difference in the late evolution of the traditional AGA-BP neural network.The non-linear function is used to adjust the variation and crossover factors.Experimental results and data analysis show that the re-gression coefficient of the precipitation model fitted with the IAGA-BP neural network is as high as 0.96,the MSE is 0.014627,and the determination coefficient R2 is 0.88,which significantly improves the accuracy and provides a new reference for the fully automated precipitation measurement optimization method.关键词
降水/压电陶瓷/神经网络/自适应遗传算法Key words
precipitation/piezoelectric ceramics/neural network/adaptive genetic algorith分类
信息技术与安全科学引用本文复制引用
蔡辉,李寅,唐乃乔..基于IAGA-BP神经网络改进的降水测量[J].计算技术与自动化,2025,44(3):128-132,140,6.
