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development-risk-prediction

于 2011-04-22 发布 文件大小:271KB
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   本文基于BP神经网络应用于预测的原理,提出预测步骤及预测可行性,探讨建立基于BP神经网络的预测模型的关键技术,包括样本的选取与预处理、输入输出变量的选取、隐层节点数的确定、初始权值和阈值的选取、激活函数、训练算法与参数的选取,最后建立合理的网络模型;结合住宅市场的实际情况,建立两类BP 神经网络预测模型:基于时间序列的趋势预测模型以及基于影响因素的回归预测模型,即分别采用神经网络趋势预测和回归预测的思路,把住宅市场的供给、需求与房价的历年数据以及其影响因素的数据分别作为学习样本,建立预测模型,进行预测。(Real estate development risk prediction )

文件列表:

基于BP神经网络的厦门楼盘走势预测.doc,569856,2011-04-21

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