摘要
针对现有各种非平稳非线性特征旋转机械运行状态预测方法适用性差、精度不高的难点问题,提出一种旋转机械运行状态优化组合模型变权重RBF预测方法,该方法通过对单一预测模型进行优选,对输入样本进行加权处理,采用径向基神经网络进行变权重组合模型动态建模,从而充分利用了已知的有效信息,强调了新旧信息对设备未来运行状态发展产生的不同影响.经实测数据验证,获得了比单一预测模型及定权重RBF组合预测方法精度更高的预测结果.该方法程序实现简便,预测精度高,对预测问题的适用性广.
In order to improve the forecast accuracy and adaptability for rotating machinery working conditions with unsteady and nonlinear features, an optimization prediction method of variable weight RBF combination model was suggested. This model was built based on variable weight RBF network. The samples were weighted according to the time to output and the combined models were selected according to the average relative error while the model built. As a result, the sufficient effective information was used, and the fact that new and old information taking different effect on the future state was stressed. The method was verified by measured data. The accuracy of variable weight RBF combination forecasting method was better than single RBF model and single weight combination forecasting methods. This method is simple to program and more adaptable on prediction with high farecast accuracy.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2013年第1期7-13,共7页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(50975020)
