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

Abstract

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.

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Funding

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

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Correspondence to Shan Liang.

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Liu, T., Liang, S., Xiong, Q. et al. Two-Stage Method for Diagonal Recurrent Neural Network Identification of a High-Power Continuous Microwave Heating System. Neural Process Lett 50, 2161–2182 (2019). https://doi.org/10.1007/s11063-019-09992-w

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Keywords

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