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Deep learning networks with rough-refinement optimization for food quality assessment

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成果类型:
期刊论文
作者:
Zhou, Jin;Zhou, Kang;Zhang, Gexiang;Liu, Qiyu;Shen, Wangyang;...
通讯作者:
Kang Zhou
作者机构:
[Zhou, Kang; Zhou, Jin] Wuhan Polytech Univ, Coll Math & Comp Sci, Wuhan 430023, Peoples R China.
[Liu, Qiyu; Zhang, Gexiang] Chengdu Univ Informat Technol, Sch Automat, Chengdu 610225, Peoples R China.
[Shen, Wangyang; Jin, Weiping] Wuhan Polytech Univ, Coll Food Sci & Engn, Wuhan 430023, Peoples R China.
通讯机构:
[Kang Zhou] C
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
语种:
英文
关键词:
Food quality assessment;Neural network rough-refinement optimization;Metaheuristic algorithm based on NNs;Data mining
期刊:
Natural Computing
ISSN:
1567-7818
年:
2023
卷:
22
期:
1
页码:
195-223
机构署名:
本校为第一机构
院系归属:
食品科学与工程学院
数学与计算机学院
摘要:
Food quality assessment is an important part of the food industry. The traditional food quality assessment technologies have the limitations of inconsistent and different technical defects for each method. Data mining technology has significant advantages in dealing with the problems of uncertainty and fuzziness. Therefore, this study proposes a food quality assessment model based on data mining, which aims to realize the standardization and consistency of food quality assessment, and can achieve or exceed the accuracy of existing technologies, so as to solve the obvious problems existing in t...

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