A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations

This paper presents a constrained backpropagation (CPROP) methodology for solving nonlinear elliptic and parabolic partial differential equations (PDEs) adaptively, subject to changes in the PDE parameters or external forcing. Unlike existing methods based on penalty functions or Lagrange multiplier...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 25; H. 3; S. 571 - 584
Hauptverfasser:	Rudd, Keith, Muro, Gianluca Di, Ferrari, Silvia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.03.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithm Applied sciences Approximation Artificial intelligence Artificial neural networks artificial neural networks (ANNs) Back propagation Boundaries Computer science; control theory; systems Connectionism. Neural networks Constraints Equations Exact sciences and technology Jacobian matrices Linear programming Mathematical analysis Mathematics Neural networks Nonlinearity Optimization Partial differential equations partial differential equations (PDEs) Sciences and techniques of general use scientific computing Training Backpropagation Dimensionality artificial neural networks (ANNs) Adaptive algorithm Initial condition scientific computing Elimination Constraint satisfaction Neural network Boundary condition Adaptive method Partial differential equation Penalty method Constrained optimization Backpropagation algorithm Parabolic equation Lagrange multiplier Elliptic equation Scientific computation partial differential equations (PDEs) Non linear effect Mathematical programming
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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