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Research on Prediction Methods of Residential Real Estate Price Based on Improved BPNN

机译：基于改进BPNN的住宅房地产价格预测方法研究

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To improve the prediction precision of residential property, the paper brings up a mixed optimizing model based on IPSO-BPNN. The model has adopted gray correlation theory to optimized the the index that influences price and use IPSO to optimize the definition of original weights and threshold value. We take the real estate market in Changsha as an example. The result shows that the speed of convergence and prediction precision of this method is superior to traditional BP neural network and IPSO-BPNN. This optimizing algorithm overcomes the drawbacks of neural network and particle swarm optimizing method, and improves the speed of convergence and the ability of searching optimum value globally.
机译：为了提高住宅物业的预测精度，纸张提出了基于IPSO-BPNN的混合优化模型。该模型采用了灰色相关理论，优化了影响价格的指数，并使用IPSO优化原始权重和阈值的定义。我们以长沙地产市场为例。结果表明，该方法的收敛速度和预测精度优于传统的BP神经网络和IPSO-BPNN。这种优化算法克服了神经网络和粒子群优化方法的缺点，提高了收敛的速度和在全球搜索最佳值的能力。

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