A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry. (1st November 2018) - Zhang, Xiao-Han; Zhu, Qun-Xiong; He, Yan-Lin; Xu, Yuan
Abstract: With the increasing complexity of energy modeling data, it becomes more and more demanding to build a robust and accurate energy analysis model using a single neural network. To deal with this problem, a novel robust ensemble model integrated extreme learning machine with multi-activation functions is proposed to develop robust and accurate energy analysis models. There are two salient features in the proposed model: one is that different effective nonlinear activation functions are adopted in extreme learning machine to enhance the ability in dealing with the high nonlinearity of energy modeling data, i.e. multi-activation functions are utilized; the other salient feature is that several single models with different effective nonlinear activation functions are combined to build an ensemble model for enhancing the performance in terms of accuracy and stability, i.e. the generalization and robustness capability of the proposed model is much improved through aggregating multiple activation functions based extreme learning machine models. To verify the performance of the proposed model, two case studies of developing energy analysis models for complex chemical processes are carried out. Simulation results demonstrate that the proposed model achieves high accuracy and good stability. Highlights: A multi-activation functions based extreme learning machine ensemble is proposed. The ensemble model utilizes different and effective nonlinear activation functions. The proposed ensemble model is developed as energy models for complex systems. The ensemble model can be accurate, stable and efficient in modeling application. Simulation results confirm the effectiveness of the proposed ensemble framework.