This paper proposes a price evaluation model based on principal component analysis(PCA)and particle swarm optimization(PSO)to address the issue of BP neural network being prone to falling into local minima when predicting second-hand car prices,as well as the correlation between price influencing factors.This article uses the 10 principal components after PCA dimensionality reduction as evaluation parameters that affect second-hand car prices.Based on BP neural network,an economic second-hand car price evaluation model is es-tablished,and particle swarm optimization algorithm is used to optimize the weights and thresholds of the net-work,further improving the prediction accuracy of the network.This model to some extent overcomes the short-comings of BP neural network and provides a reference for the evaluation of second-hand car prices.
关键词
经济型二手车/估价模型/BP神经网络/主成分分析(PCA)/粒子群算法(PSO)
Key words
Economical used car/Valuation model/BP neural network/Principal Component Analysis(PCA)/Particle Swarm Optimization(PSO)
维普
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