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
Online-Zugang:	Volltext
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