基于改进极限学习机的软测量建模方法

针对生物发酵过程中一些生物参量难以测量的问题,提出一种基于改进极限学习机(IELM)的软测量建模方法。该方法通过最小二乘方法和误差反馈原理计算出最优的网络输入到隐含层的学习参数,以提高模型的稳定性和预测精度。通过双对角化方法计算出最优的输出权值,解决输出矩阵的病态问题,进一步提高模型的稳定性。将所提方法应用于红霉素发酵过程生物量浓度的软测量。结果表明,与ELM、PL-ELM、IRLS-ELM软测量建模方法相比,IELM在线软测量建模方法具有更高的预测精度和更强的泛化能力。

Soft sensor modeling based on improved extreme learning machine algorithm

To solve the problem that biomass concentration is difficult to measure directly in the fermentation process,a soft sensor modeling method based on Improved Extreme Learning Machine(IELM)is proposed.The least squares method is combined with the ELM algorithm to calculate the optimal learning parameters.And the training error is used as feedback input to improve the stability and prediction of ELM.In order to further improve the stability of the model,the Lanczos Bidiagonalization(LBD)is used to calculate the output weights.The proposed modeling method is used to construct a novel soft sensor model for the erythromycin fermentation process.Compared with ELM、IRLS-ELM and PL-ELM model,IELM model has higher prediction accuracy and stronger generalization capability.