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ICPM: An Intelligent Compound Prediction Model Based on GA and GRNN

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成果类型:
会议论文
作者:
Chen F.;Zhang C.
通讯作者:
Zhang, C.
作者机构:
[Zhang C.; Chen F.] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430000, China
通讯机构:
[Zhang, C.] S
School of Mathematics and Computer Science, China
语种:
英文
关键词:
Generalized regression neural network;Genetic Algorithm;Heavy metal content prediction;Parameter optimization;Small sample prediction
期刊:
Communications in Computer and Information Science
ISSN:
1865-0929
年:
2021
卷:
1422
页码:
105-118
会议名称:
7th International Conference on Artificial Intelligence and Security, ICAIS 2021
会议时间:
19 July 2021 through 23 July 2021
主编:
Sun X.Zhang X.Xia Z.Bertino E.
出版者:
Springer Science and Business Media Deutschland GmbH
ISBN:
9783030786144
基金类别:
Funding Statement. This work was supported in part by the Major Technical Innovation Projects of Hubei Province under Grant 2018ABA099, in part by the National Science Fund for Youth of Hubei Province of China under Grant 2018CFB408, in part by the Natural Science Foundation of Hubei Province of China under Grant 2015CFA061, in part by the National Nature Science Foundation of China under Grant 61272278, and in part by Research on Key Technologies of Intelligent Decision-making for Food Big Data under Grant 2018A01038.
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
In order to reduce the prediction error of the heavy metal content of farmland soil by General Regression Neural Network (GRNN), an Intelligent Compound Prediction Model (ICPM) was proposed. As the result of Genetic Algorithm optimization is good or bad, it mainly depends on whether it can guarantee the diversity of the population in the optimization process. Based on this, an Improved Genetic Algorithm (IGA) is proposed. IGA introduces the probability adjustment of the sine function transformation and the better gene replacement criterion into...

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