桂林理工大学学报Issue(3)：589-594,6.DOI:10.3969/j.issn.1674-9057.2014.03.031
基于预测模型的BP_Adaboost算法改进
Improvement of BP_Adaboost algorithm based on prediction model
韩韬 1陈晓辉1
作者信息
- 1. 桂林理工大学信息科学与工程学院，广西桂林 541004
- 折叠
摘要
Abstract
The prediction problem is the core of large data.The existing BP_Adaboost algorithm is a fusion in BP neural network model of prediction model algorithm.As the accuracy of BP_Adaboost algorithm is not high, the BP_Adaboost algorithm is improved in our stndy.BP neural network model is put as a weak predictor,and the strong predictor of multiple BP neural network composed of weak predictor is obtained by BP_Adaboost algo-rithm.Genetic algorithm is used for each BP neural network prediction model for optimization.When optimized BP neural network model as a new weak predictor,and through the BP_Adaboost algorithm,the BP neural net-work by genetic algorithm optimization of weak predictor is composed of strong predictor model.From 2 000 groups of random experimental data,the prediction accuracy to verify the improved algorithm leads to improved BP_Adaboost algorithm simulation with Matlab program.The result is compared with the BP_Adaboost algo-rithm before improvement.From the result of running program,the prediction of the improved BP_Adaboost al-gorithm possesses higher precision.关键词
BP_Adaboost算法/遗传算法/强预测模型/BP神经网络Key words
BP_Adaboost algorithm/genetic algorithm/strong prediction model/BP neural network分类
信息技术与安全科学引用本文复制引用
韩韬,陈晓辉..基于预测模型的BP_Adaboost算法改进[J].桂林理工大学学报,2014,(3):589-594,6.
