Application of Neural Network in National Economic Forecast

Xiaofeng Yan, Jie Zhao
{"title":"Application of Neural Network in National Economic Forecast","authors":"Xiaofeng Yan, Jie Zhao","doi":"10.1109/ICIVC.2018.8492863","DOIUrl":null,"url":null,"abstract":"Prediction is a common method in data mining. In the prediction method, it can be divided into linear prediction and nonlinear prediction. The multiple linear regression method belongs to the linear regression method, and the neural network algorithm belongs to nonlinear prediction. The neural network algorithm belongs to the computational intelligence algorithm. It depends on the complexity of the system and connects the relations between the internal nodes of the neural network through the weights to process the data information. Based on multiple linear regression and neural network algorithms, this paper proposes a predictive model based on multiple linear regression and neural network, and uses this model to study national economic data. The prediction model proposed in this paper is realized by using the linear prediction result as the input neuron of the neural network. The neural network used in this paper is a radial basis function neural network, hereinafter referred to as RBF neural network.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC.2018.8492863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Prediction is a common method in data mining. In the prediction method, it can be divided into linear prediction and nonlinear prediction. The multiple linear regression method belongs to the linear regression method, and the neural network algorithm belongs to nonlinear prediction. The neural network algorithm belongs to the computational intelligence algorithm. It depends on the complexity of the system and connects the relations between the internal nodes of the neural network through the weights to process the data information. Based on multiple linear regression and neural network algorithms, this paper proposes a predictive model based on multiple linear regression and neural network, and uses this model to study national economic data. The prediction model proposed in this paper is realized by using the linear prediction result as the input neuron of the neural network. The neural network used in this paper is a radial basis function neural network, hereinafter referred to as RBF neural network.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
神经网络在国民经济预测中的应用
预测是数据挖掘中的一种常用方法。在预测方法中,可分为线性预测和非线性预测。多元线性回归方法属于线性回归方法,神经网络算法属于非线性预测。神经网络算法属于计算智能算法。它根据系统的复杂程度,通过权值连接神经网络内部节点之间的关系,对数据信息进行处理。本文基于多元线性回归和神经网络算法,提出了一种基于多元线性回归和神经网络的预测模型,并利用该模型对国民经济数据进行了研究。本文提出的预测模型是利用线性预测结果作为神经网络的输入神经元来实现的。本文使用的神经网络是径向基函数神经网络,以下简称RBF神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Investigation of Skeleton-Based Optical Flow-Guided Features for 3D Action Recognition Using a Multi-Stream CNN Model Research on the Counting Algorithm of Bundled Steel Bars Based on the Features Matching of Connected Regions Hybrid Change Detection Based on ISFA for High-Resolution Imagery Scene Recognition with Convolutional Residual Features via Deep Forest Design and Implementation of T-Hash Tree in Main Memory Data Base
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1