DocumentCode
292023
Title
Sensitivity analysis of neural models
Author
Lamy, D. ; Borne, P.
Author_Institution
Lab. de Autom., Ecole Centrale de Lille, Villeneuve d´´Ascq, France
Volume
2
fYear
1994
fDate
2-5 Oct 1994
Firstpage
1481
Abstract
This paper investigates the sensitivity of neural models to weights perturbation in a system identification task. Analytical expression for sensitivity is derived from a notation based on Kronecker product and vector valued function of matrix. Experimental results highlight this sensitivity measure when investigating model structure. A comparison with statistical sensitivity results confirms usefulness of our approach. Search for minimum output sensitivity appears to be a nice indicator for proper model order choice
Keywords
identification; modelling; neural nets; sensitivity analysis; Kronecker product; analytical expression; minimum output sensitivity; model order choice; neural model sensitivity analysis; statistical sensitivity; system identification; vector-valued function; weights perturbation; Art; Delay; Multi-layer neural network; Neural networks; Nonhomogeneous media; Polynomials; Predictive models; Sensitivity analysis; System identification; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location
San Antonio, TX
Print_ISBN
0-7803-2129-4
Type
conf
DOI
10.1109/ICSMC.1994.400055
Filename
400055
Link To Document