DocumentCode :
1625967
Title :
Sensitivity and perturbation analysis of artificial feedback neural networks
Author :
Michel, Anthony N. ; Wang, Kaining
Author_Institution :
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
fYear :
1992
Abstract :
Summary form only given. The authors apply the result established by A.N. Michel et al. (1992) to systems of differential inequalities. By using the differential inequalities to dominate a class of nonlinear systems, they obtain some new results on robust stability of the nonlinear systems. They show that, when applied to linear systems, these results yield robustness criteria which are considerably simpler than corresponding existing robust stability tests. However, the principal objective of the present work is to apply the above results in the study of uncertainty issues of artificial feedback neural networks, such as Hopfield neural networks. The authors attempt to provide sufficient conditions to ensure robust stability properties of equilibria of neural networks
Keywords :
feedback; neural nets; nonlinear systems; perturbation techniques; sensitivity analysis; stability criteria; Hopfield neural networks; artificial feedback neural networks; differential inequalities; neural network equilibria; nonlinear systems; perturbation analysis; robust stability; sensitivity analysis; Artificial neural networks; Hopfield neural networks; Linear systems; Neural networks; Neurofeedback; Nonlinear systems; Robust stability; Sufficient conditions; System testing; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1992., IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-0720-8
Type :
conf
DOI :
10.1109/ICSMC.1992.271601
Filename :
271601
Link To Document :
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