基于遗传算法和BP神经网络的房价预测分析  

Analysis of House Price Prediction Based on Genetic Algorithm and BP Neural Network

作　　者：高玉明[1] 张仁津[1,2] 

机构地区：[1]贵州师范大学数学与计算机科学学院,贵阳550001 [2]贵州师范大学多媒体CAI研究所,贵阳550001

出　　处：《计算机工程》2014年第4期187-191,共5页Computer Engineering

基　　金：国家自然科学基金资助项目(41161065);贵州省省长基金资助项目(黔省专合字(2009)115);贵州省科技创新人才团队基金资助项目(黔科合人才团队(2012)4009)

摘　　要：针对BP神经网络存在易陷入局部极小值、收敛速度慢等问题,提出用遗传算法优化BP神经网络并用于房价预测。采用BP神经网络建立房价预测模型。利用遗传算法对BP神经网络的初始权值和阈值进行优化。选取1998年2011年贵阳市的房价及其主要影响因素作为实验数据,分别对传统的BP神经网络和经过遗传算法优化后的BP神经网络进行训练和仿真实验,结果表明,与传统的BP神经网络预测模型相比,经过遗传算法优化后的BP神经网络预测模型能加快网络的收敛速度,提高房价的预测精度。Aiming at the problem of BP neural network, namely easily getting stuck in a local minimum and slow convergence rate, using Genetic Algorithm（GA） to optimize BP neural network is proposed to predict house price. This paper forms prediction model for the house price by using BP neural network. The GA optimizes the connection weights and structure of BP neural network. The house price in Guiyang and its main influencing factors are selected from 1998 to 2011. The historical data are used as the experimental data, to train and simulate respectively through traditional BP neural network and BP neural network optimized by GA. Experimental results show that, compared with the traditional BP neural network, the BP neural network optimized by GA can make convergence rate quicker, and improve the prediction accuracy.

关 键 词：BP神经网络 遗传算法 优化 权值 房价 预测模型 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]