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Hybrid Genetic Algorithm and Support Vector Machine Performance in Public Fiscal Revenue Prediction

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成果类型:
会议论文
作者:
Yang, Hua;He, Jiaqi;Jiang, Feng*
通讯作者:
Jiang, Feng
作者机构:
[Yang, Hua] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.
[Jiang, Feng; He, Jiaqi] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Hubei, Peoples R China.
通讯机构:
[Jiang, Feng] Z
Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Hubei, Peoples R China.
语种:
英文
关键词:
Public fiscal revenue;support vector machine;genetic algorithm;gray neural network;BP neural network
期刊:
Chinese Control Conference
ISSN:
1934-1768
年:
2018
卷:
2018-July
页码:
4495-4499
会议名称:
第37届中国控制会议
会议时间:
2018-07-25
会议地点:
中国湖北武汉
基金类别:
National Natural Science Foundation (NNSF) of ChinaNational Natural Science Foundation of China (NSFC) [61773401, 61304067, 11601524]; Natural Science Foundation of Hubei Province of ChinaNatural Science Foundation of Hubei Province [2013CFB443]; Foundation of Hubei Province of China [17G024, 2017132]
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:
Local public fiscal revenue (PFR) is an important indicator to measure the level of local economic development. Accurate prediction of local public fiscal revenue can provide theoretical support for governments and relevant departments to make scientific decisions. Firstly, we use the principal component analysis to reduce the dimensions of variables. Secondly, we predict Harbin public fiscal revenue by using hybrid genetic algorithm (GA) and support vector machine (SVM) model. Then we compare the results with a back propagation neural network model and a gray neural network model optimized wi...

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