CAJ | 학술논문

针对在对聚丙烯熔融指数进行预测时优势数据和优势变量不突出影响预测精度、数据平滑度不够影响泛化性能的问题，提出了基于多技术融合加权平滑的径向基函数神经网络预报模型。综合运用了在时间尺度基于空间欧氏距离加权、在变量维度上基于灰色关联和线性回归误差加权两种数据加权方案，基于过程变量差分序列欧氏距离的平滑和局部线性平滑两种数据平滑方案，解决了模型精度和泛化性低的问题。为进一步改进模型性能，采用带误差补偿的非线性自回归滑动平均模型框架和径向基函数神经网络，利用自校正预测控制算法和分段线性变学习率算法，对模型进行优化。结合某厂真实数据对模型进行验证，预报结果在泛化集上为：平均相对误差( MRE )1.32%、均方根误差(RMSE)0.0459。与其他方法进行了详细的比较分析，结果表明该模型具有良好的预报精度和泛化性能，在大时滞工业过程领域具有一定的应用价值。
침대재대취병희용융지수진행예측시우세수거화우세변량불돌출영향예측정도、수거평활도불구영향범화성능적문제，제출료기우다기술융합가권평활적경향기함수신경망락예보모형。종합운용료재시간척도기우공간구씨거리가권、재변량유도상기우회색관련화선성회귀오차가권량충수거가권방안，기우과정변량차분서렬구씨거리적평활화국부선성평활량충수거평활방안，해결료모형정도화범화성저적문제。위진일보개진모형성능，채용대오차보상적비선성자회귀활동평균모형광가화경향기함수신경망락，이용자교정예측공제산법화분단선성변학습솔산법，대모형진행우화。결합모엄진실수거대모형진행험증，예보결과재범화집상위：평균상대오차( MRE )1.32%、균방근오차(RMSE)0.0459。여기타방법진행료상세적비교분석，결과표명해모형구유량호적예보정도화범화성능，재대시체공업과정영역구유일정적응용개치。
In view of the problems that not highlighted predominant data and variables affect the prediction accuracy and not enough data smoothness influences the generalization performance in the prediction of PolyPropylene ( PP ) Melt Index ( MI) , this paper proposed a forecasting model of MI based on Radial Basis Function Neural Network ( RBFNN) with weighting and smoothing of multi-technology integration. The proposed model integratedly applied two data weighting schemes: weighting based on space Euclidean distance in the time scale, weighting based on grey correlation and linear autoregression error in the variable dimension, and also applied two data smoothing methods: smoothing based upon the Euclidean distance of process variable differential sequence and partial linear smoothing, to solve the problems of low prediction precision and generalization ability. To further improve the forecasting capability of the model, based on the Nonlinear Autoregressive Moving Average ( NARMA) model framework with error compensation and the RBFNN, the paper used the self-tuning predictive control algorithm and the piecewise-linear alterable learning rate algorithm to optimize this model. The presented model is validated by the real data from a plant and the prediction results on the generalization database are as follows: Mean Relative Error ( MRE) is 1. 32%, Root Mean Square Error ( RMSE) is 0. 045 9. Compared and analysed detailedly with the report in the literature, the results show that the proposed model in the paper has an excellent forecasting accuracy and generalization ability, and has a certain application value in the industrial process with large time delay.