Evidence suggests that the prognostic ability of the new model with high stability , when hidden nodes changing nearby input nodes and training times changing at the certain extent , is significantly better than traditional step wise regression model mainly due to the new model condensing the more forecasting information , properly utilizing the ability of ann self - adaptive learning and nonlinear mapping . but the linear regression technique only selects several predictors by the f value , many predictors information with high relative coefficients is not included . so the new model proposed in this paper is effective and is of a very good prospect in the atmospheric sciences fields 進(jìn)一步深入分析研究發(fā)現(xiàn),本文提出的這種基于主成分的神經(jīng)網(wǎng)絡(luò)預(yù)報模型,預(yù)報精度明顯高于傳統(tǒng)的逐步回歸方法,其主要原因是這種新的預(yù)報模型集中了眾多預(yù)報因子的預(yù)報信息,并有效地利用了人工神經(jīng)網(wǎng)絡(luò)方法的自組織和自適應(yīng)的非線性映射能力;而傳統(tǒng)的逐步回歸方法是一種線性方法,并且逐步回歸方法只是根據(jù)f值大小從眾多預(yù)報因子中選取幾個預(yù)報因子,其余預(yù)報因子的預(yù)報信息被舍棄。