Partial Differential Equations Numerical Modeling Using Dynamic Neural Networks

被引：0

作者：

Fuentes, Rita ^{[1
]}

Poznyak, Alexander ^{[1
]}

Chairez, Isaac ^{[2
]}

Poznyak, Tatyana ^{[3
]}

机构：

[1] CINVESTAV, IPN, Dept Automat Control, Mexico City 14000, DF, Mexico

[2] UPIBI, IPN, Bioelect Dept, Mexico City 14000, DF, Mexico

[3] ESIQIE IPN, IPN, Postgraduete Div, Mexico City 14000, DF, Mexico

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II | 2009年 / 5769卷

关键词：

Neural Networks; Adaptive identification; Distributed Parameter Systems; Partial Differential Equations and Practical Stability;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a strategy based on differential neural networks (DNN) for the identification of the parameters in a mathematical model described by partial differential equations is proposed. The identification problem is reduced to finding an exact expression for the weights dynamics using the DNNs properties. The adaptive laws for weights ensure the convergence of the DNN trajectories to the PDE states. To investigate the qualitative behavior of the suggested methodology, here the non parametric modeling problem for a distributed parameter plant is analyzed: the anaerobic digestion system

引用

页码：552 / +

页数：4

共 17 条

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