DocumentCode
323377
Title
The stability of the analog Hopfield neural network with iterative dynamics
Author
Wang, Lipo
Author_Institution
Sch. of Comput. & Math., Deakin Univ., Clayton, Vic., Australia
Volume
1
fYear
1997
fDate
28-31 Oct 1997
Firstpage
449
Abstract
We first present an example which shows that a general dynamic system with an infinite number of fixed points may never stabilize, even when there exists a Lyapunov function which strictly decreases along all possible trajectories. We then explain why the analog Hopfield neural network with iterative dynamics always converges to a fixed point, regardless of how many fixed points it has
Keywords
Hopfield neural nets; Lyapunov methods; analogue processing circuits; convergence; functions; iterative methods; stability; analog Hopfield neural net; convergence; fixed points; general dynamic system; iterative dynamics; stability; strictly decreasing Lyapunov function; trajectories; Convergence; Counting circuits; Hopfield neural networks; Microwave integrated circuits; Neural networks; Neurons; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-4253-4
Type
conf
DOI
10.1109/ICIPS.1997.672821
Filename
672821
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