.png){kind=icon}
返回
Mango wine making process optimization based on artificial intelligence deep learning technology
.png){kind=icon}
PRE
AI
DOI：10.1111/exsy.13032.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE-BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE-BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process.
Keyword:
auto-encoder
deep learning
electronic nose system
optimization
SSAE-BPNN
期刊
IF:
2.3
论文数:
2.6K
被引数:
3.8K
机构
暂无机构信息
引用论文
.png){kind=icon}
Hypergraph+: An Improved Hypergraph-Based Task-Scheduling Algorithm for Massive Spatial Data Processing on Master-Slave PlatformsHypergraph+: 一种改进的基于超图的面向主从平台大规模空间数据处理的任务调度算法
OAAI
OAAI分享收藏
查看更多内容