基于GA-BP神经网络的成都市房价预测模型

Prediction Model of House Price in Chengdu Based on GA-BP Neural Network

在采用BP神经网络进行预测的时候,很可能会出现收敛速度慢和预测精度不足等问题。而遗传算法的全局搜索能力,能够调整BP神经网络的初始权值和阈值,达到效果优化。分别用BP神经网络和GA-BP神经网络建立了房价预测模型,选择成都市房价及其主要相关影响因素的数据进行实验,对模型的最终预测效果进行对比。实验结果显示,改进后的GA-BP神经网络比BP神经网络预测精度高、收敛速度快。

When BP neural network is used for prediction, problems such as slow convergence speed and insufficient prediction accuracy may occur. The global search ability of genetic algorithm can adjust the initial weight and threshold of BP neural network to optimize the effect. The house price prediction models of BP neural network and GA-BP neural network are established respectively. Select the data of Chengdu housing price and its related main influencing factors for the experiment, and compare the final prediction effect of the model. The experimental results show that the improved GA-BP neural network has higher prediction accuracy and faster convergence speed than BP neural network.