基于LSTM和IGA-BP的酒精度预测模型

目的:解决 目前分段摘酒过程依赖人工"看花摘酒",酒精度检测不准确的问题.方法:设计搭建基于酒精度建模的分段摘酒系统,研究采集音叉在不同模态不同浓度酒精溶液内的音叉频率值、音叉内置温度值,酒精溶液温度值和动态条件下泵转速值,基于最小均方算法(LMS)和长短期记忆网络(LSTM)实现音叉频率自适应滤波和动态补偿,基于改进遗传算法优化BP神经网络(IGA-BP)建立酒精度预测模型.结果:模型在迭代次数和预测精度上优于传统遗传算法优化BP神经网络和BP神经网络建立的酒精度预测模型,酒精度平均预测误差为0.381.结论:基于改进遗传算法优化BP神经网络(IGA-BP)建立酒精度数预...

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Published in食品与机械 Vol. 38; no. 5; pp. 71 - 77
Main Authors 张建华, 商建伟, 王唱, 赵岩, 李克祥, 李祥利
Format Journal Article
LanguageChinese
Published 河北工业大学机械工程学院,天津 300401 2022
Subjects
长短期记忆网络
遗传算法
酒精度
BP神经网络
摘酒
Online AccessGet full text
ISSN1003-5788
DOI10.13652/j.spjx.1003.5788.2022.90072

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Abstract 目的:解决 目前分段摘酒过程依赖人工"看花摘酒",酒精度检测不准确的问题.方法:设计搭建基于酒精度建模的分段摘酒系统,研究采集音叉在不同模态不同浓度酒精溶液内的音叉频率值、音叉内置温度值,酒精溶液温度值和动态条件下泵转速值,基于最小均方算法(LMS)和长短期记忆网络(LSTM)实现音叉频率自适应滤波和动态补偿,基于改进遗传算法优化BP神经网络(IGA-BP)建立酒精度预测模型.结果:模型在迭代次数和预测精度上优于传统遗传算法优化BP神经网络和BP神经网络建立的酒精度预测模型,酒精度平均预测误差为0.381.结论:基于改进遗传算法优化BP神经网络(IGA-BP)建立酒精度数预测模型具有合理性.
AbstractList 目的:解决 目前分段摘酒过程依赖人工"看花摘酒",酒精度检测不准确的问题.方法:设计搭建基于酒精度建模的分段摘酒系统,研究采集音叉在不同模态不同浓度酒精溶液内的音叉频率值、音叉内置温度值,酒精溶液温度值和动态条件下泵转速值,基于最小均方算法(LMS)和长短期记忆网络(LSTM)实现音叉频率自适应滤波和动态补偿,基于改进遗传算法优化BP神经网络(IGA-BP)建立酒精度预测模型.结果:模型在迭代次数和预测精度上优于传统遗传算法优化BP神经网络和BP神经网络建立的酒精度预测模型,酒精度平均预测误差为0.381.结论:基于改进遗传算法优化BP神经网络(IGA-BP)建立酒精度数预测模型具有合理性.
Author 商建伟
李祥利
王唱
张建华
赵岩
李克祥
AuthorAffiliation 河北工业大学机械工程学院,天津 300401
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LI Ke-xiang
LI Xiang-li
SHANG Jian-wei
ZHAO Yan
WANG Chang
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Keywords 长短期记忆网络
遗传算法
酒精度
BP神经网络
摘酒
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Snippet 目的:解决 目前分段摘酒过程依赖人工"看花摘酒",酒精度检测不准确的问题.方法:设计搭建基于酒精度建模的分段摘酒系统,研究采集音叉在不同模态不同浓度酒精溶液内的音叉频...
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Title 基于LSTM和IGA-BP的酒精度预测模型
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