Two-Stage Method for Diagonal Recurrent Neural Network Identification of a High-Power Continuous Microwave Heating System

  • Tong Liu
  • Shan LiangEmail author
  • Qingyu Xiong
  • Kai Wang


This paper proposes a diagonal recurrent neural network (DRNN) based identification scheme to handle the complexity and nonlinearity of high-power continuous microwave heating system (HPCMHS). The new DRNN design involves a two-stage training process that couples an efficient forward model selection technique with gradient-based optimization. In the first stage, an impact recurrent network structure is obtained by a fast recursive algorithm in a stepwise forward procedure. To ensure stability, update rules are further developed using Lyapunov stability criterion to tune parameters of reduced size model at the second stage. The proposed approach is tested with an experimental regression problem and a practical HPCMHS identification, and the results are compared with four typical network models. The results show that the new design demonstrates improved accuracy and model compactness with reduced computational complexity over the existing methods.


Diagonal recurrent neural network High-power continuous microwave heating system Fast recursive algorithm Lyapunov stability criterion Computational complexity 



This work was supported by the National Natural Science Foundation of China under Grant 61771077.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tong Liu
    • 1
    • 2
  • Shan Liang
    • 1
    • 2
    Email author
  • Qingyu Xiong
    • 3
  • Kai Wang
    • 1
    • 2
  1. 1.Key Laboratory of Complex System Safety and Control Ministry of Education, MOEChongqing UniversityChongqingChina
  2. 2.School of AutomationChongqing UniversityChongqingChina
  3. 3.School of Software EngineeringChongqing UniversityChongqingChina

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