The Application of BP Neural Network Learning Algorithm Based on the Particle Swarm Optimization

被引：1

作者：

Sun, Zhihong ^{[1
]}

Wang, Jun ^{[2
]}

Xu, Baoji ^{[3
]}

机构：

[1] Air Force Logist Coll, Dept Fdn, Math Staff Room, Xuzhou, Jiangsu, Peoples R China

[2] Air Force Logist Coll, Dept Bomb, Xuzhou, Jiangsu, Peoples R China

[3] Air Force Logist Coll, Dept Finance, Xuzhou, Jiangsu, Peoples R China

来源：

MECHATRONICS AND INTELLIGENT MATERIALS III, PTS 1-3 | 2013年 / 706-708卷

关键词：

BP neural network; particle swarm optimization (PSO); real estate; prediction model;

D O I：

10.4028/www.scientific.net/AMR.706-708.2057

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The development of real estate has been affected by various social factors, including economic factors. BP neural network can more accurately forecast the trend of real estate industry according to economic development indicators. But BP neural network is slow convergence in the training process, and easily falls into local optimum. The BP neural network learning algorithm based on the particle swarm optimization (PSO) optimizes the weights and thresholds of the network by PSO algorithm, then to train BP neural network. The experimental results show that the performance of this new algorithm is better than BP neural network, but also has good convergence.

引用

页码：2057 / +

页数：2

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