DocumentCode
296111
Title
Approximating elliptic PDE by perturbation of neural dynamics
Author
Cheung, Leung Fu ; Li, Leong Kwan
Author_Institution
Dept. of Appl. Math., Hong Kong Polytech., Hong Kong
Volume
4
fYear
1995
fDate
Nov/Dec 1995
Firstpage
1733
Abstract
After finite difference discretization, solving elliptic partial differential equation (PDE) numerically would be equivalent to solving a positive definite linear system Ax=b. By rescaling the linear system so as to bound the solution x around the origin, we introduce the term Ax-b as a perturbation to an artificial neural network and show that the equilibrium state around the origin is an approximate solution
Keywords
approximation theory; finite difference methods; linear systems; mathematics computing; partial differential equations; perturbation techniques; recurrent neural nets; approximate solution; elliptic partial differential equation; equilibrium state; finite difference discretization; linear system; neural dynamics; perturbation; recurrent neural network; Artificial neural networks; Boundary conditions; Differential equations; Finite difference methods; Finite element methods; Linear systems; Neural networks; Neurons; Partial differential equations; Recurrent neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.488882
Filename
488882
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