An improved bp neural network algorithm based on factor analysis

作者：Ding Shifei^*; Jia Weikuan; Su Chunyang; Liu Xiaoliang; Chen Jinrong

来源：Journal of Convergence Information Technology, 2010, 5(4): 103-108.

DOI：10.4156/jcit.vol5.issue4.11

• Artificial Neural Network
• Back-propagation neural networks
• BP algorithm
• BP neural networks
• Computing time
• Factor analysis
• Feature variable
• High dimensional data
• Improved BP neural network
• Large-scale computing
• Low dimensional
• Multivariate analysis methods
• Network inputs
• Network structures
• Network training
• Prediction precision
• Running time
• Storage spaces
• Structure features
• Training network

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has the ability of large scale computing and has unique advantages when dealing with nonlinear high dimensional data. But when we manipulate high dimensional data with BP neural network, many feature variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the the accuracy of recognition finally. Factor analysis (FA) is a multivariate analysis method which transforms many feature variables into few synthetic variables. Aiming at the characteristics that the samples processed have more feature variables, combining with the structure feature of BP neural network, a FA-BP neural network algorithm is proposed. Firstly we reduce the dimensionality of the feature factor using FA, and then regard the features reduced as the input of the BP neural network, carry on network training and simulation with low dimensional data that we get. This algorithm here can simplify the network structure, improve the velocity of convergence, and save the running time. Then we apply the new algorithm in the field of pest prediction to emulate. The results show that under the prediction precision is not reduced, the error of the prediction value is reduced by using the new algorithm, and therefore the algorithm is effective.

• 出版日期2010
• 单位中国矿业大学（北京）