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

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...

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Veröffentlicht in:Neural processing letters Jg. 50; H. 3; S. 2161 - 2182
Hauptverfasser: Liu, Tong, Liang, Shan, Xiong, Qingyu, Wang, Kai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2019
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Complex Systems
Complexity
Computational Intelligence
Computer Science
Efficiency
Genetic algorithms
Heating
Identification
Microwave heating
Model accuracy
Neural networks
Neurons
Optimization
Partial differential equations
Recurrent neural networks
Stability criteria
Systems design
Systems stability
Teaching methods
Lyapunov stability criterion
Fast recursive algorithm
Diagonal recurrent neural network
Computational complexity
High-power continuous microwave heating system
ISSN:1370-4621, 1573-773X
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-019-09992-w