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Multi-model Modeling of Heating Furnace System Based on FCM and GA Optimization ElasticNet-SVR

  • Zhengguang Xu
  • Weijian Kong
  • Mushu Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 529)

Abstract

Aiming at the characteristics of non-linear, time-varying and wide-ranging working conditions of heating furnace, with the improvement of control requirements for actual system prediction, single model modeling has the problems of large amount of calculation and poor accuracy. A multi-model modeling method is proposed in this paper. This method first divides the actual data of the heating furnace system into training set, validation set and test set, and uses FCM clustering to divide the training set into different working conditions; The Elastic Network (ElasticNet) and support Vector Machine regression (SVR) models are established in each local condition, and the optimal model of each local condition is selected from the two models by the validation set; use genetic algorithm (GA) to obtain the optimal weight of each local model, finally construct a model suitable for the global. This modeling method has a good global adaptability to the identification process. The veracity of the model is verified on the test set, and good results are obtained.

Keywords

Multi-model modeling FCM clustering ElasticNet-SVR model Genetic algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Automation & Electrical EngineeringUniversity of Science and Technology BeijingBeijingChina

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