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。

关键词: BP神经网络, 遗传算法, 优化, 权值, 房价, 预测模型

Abstract: 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.

Key words: BP neural network, Genetic Algorithm(GA), optimization, weight, house price, prediction model

中图分类号: 

• TP183