区域经济预测的GPCA和优化小波网络组合模型研究 |
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引用本文: | 周建新,;付传秀.区域经济预测的GPCA和优化小波网络组合模型研究[J].佳木斯工学院学报,2014(3):459-461. |
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作者姓名: | 周建新 ;付传秀 |
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作者单位: | [1]皖西学院应用文科实训中心,安徽六安237012; [2]皖西学院金融与数学学院,安徽六安237012 |
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基金项目: | 六安市定向委托皖西学院市级研究项目(2012LW020); 安徽高校省级科学研究项目(KJ2013B332) |
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摘 要: | 构建全局主成分分析(GPCA)和优化小波神经网络的组合模型,对中国区域经济发展水平进行预测.首先借助GPCA获得区域经济发展水平的全局主成分分值、综合评价值,作为优化小波网络的输入、目标输出;然后构建遗传-粒子群算法优化的小波网络预测模型.通过仿真,得到较满意的结果,表明区域经济水平预测的组合模型是有效和实用的.
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关 键 词: | 区域经济 全局主成分分析(GPCA) 优化小波网络 组合模型 |
Study on Prediction Model for Regional Economy Based on GPCA and Optimized WNN |
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Affiliation: | ZHOU Jian-xin, FU Chuan-xiu ( 1. Experiment Center of Liberal Arts, West Anhui University, Luan 237012, China;2. College of Finance and Mathematics, West Anhni University, Luan 237012, China) |
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Abstract: | The model of Global Principal Component Analysis ( GPCA ) and optimized Wavelet Neural Network ( WNN) was suggested to predict the regional economic development level in China .First, the global principal component scores and the comprehensive evaluation value were obtained by means of GPCA , as the in-put of optimized WNN.Then, the prediction model of GAPSO -WNN was constructed.The result of simulation test proved the validity and practicability of the model . |
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Keywords: | regional economy global principal component analysis optimized WNN combined model |
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