微处理机
微處理機
미처리궤
MICROPROCESSORS
2014年
3期
52-55
,共4页
尘肺病%灰色模型%BP神经网络%遗传算法%仿真
塵肺病%灰色模型%BP神經網絡%遺傳算法%倣真
진폐병%회색모형%BP신경망락%유전산법%방진
Dust-pulmonary disease%Grey model%BP neural network%Genetic algorithm%Simulation
为了提高尘肺病的预测准确性,针对尘肺病历史数据少、不确定的特点,采用多种数据挖掘技术进行建模,提出一种基于 GM-BPNN 的尘肺病组合预测模型。首先利用灰色模型GM(1,1)对尘肺病进行预测,然后采用BP神经网络对GM(1,1)预测结果进行修正,并采用遗传算法优化BP神经网络的初始权值和阈值,最后对1981~2006年的尘肺病例进行仿真测试。仿真结果表明GM-BPNN很好地解决了尘肺病预测过程中的小样本、非线性问题,相对于单一预测模型,提高了尘肺病的预测精度。
為瞭提高塵肺病的預測準確性,針對塵肺病歷史數據少、不確定的特點,採用多種數據挖掘技術進行建模,提齣一種基于 GM-BPNN 的塵肺病組閤預測模型。首先利用灰色模型GM(1,1)對塵肺病進行預測,然後採用BP神經網絡對GM(1,1)預測結果進行脩正,併採用遺傳算法優化BP神經網絡的初始權值和閾值,最後對1981~2006年的塵肺病例進行倣真測試。倣真結果錶明GM-BPNN很好地解決瞭塵肺病預測過程中的小樣本、非線性問題,相對于單一預測模型,提高瞭塵肺病的預測精度。
위료제고진폐병적예측준학성,침대진폐병역사수거소、불학정적특점,채용다충수거알굴기술진행건모,제출일충기우 GM-BPNN 적진폐병조합예측모형。수선이용회색모형GM(1,1)대진폐병진행예측,연후채용BP신경망락대GM(1,1)예측결과진행수정,병채용유전산법우화BP신경망락적초시권치화역치,최후대1981~2006년적진폐병례진행방진측시。방진결과표명GM-BPNN흔호지해결료진폐병예측과정중적소양본、비선성문제,상대우단일예측모형,제고료진폐병적예측정도。
Aiming at the problem of fewer historical data with uncertainty characteristics,in order to improve the accuracy of prediction for dust-pulmonary disease,the paper proposes one prediction model based on the GM-BPNN by using many data mining technology.Firstly,GM(1 ,1 )is used to predict the dust-pulmonary disease,and then BP neural network is used to modify the prediction results of GM (1 ,1 )which initial weights and thresholds of the BP neural network are optimized by genetic algorithm,and finally the test for dust-pulmonary disease case from 1981 to 2006 is conducted.The simulation results show that GM-BPNN is a good solution to the problems of small sample and nonlinear and the proposed model improves the precision of prediction for dust-pulmonary disease.