DocumentCode
2473813
Title
Global asymptotic stability of BAM neural networks with mixed delays
Author
Zhao, Bao-zhu
Author_Institution
Found. Dept., Sichuan TOP Vocational Inst. of Inf. Technol., Chengdu, China
fYear
2010
fDate
17-19 Dec. 2010
Firstpage
133
Lastpage
137
Abstract
This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with mixed delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter; and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results derived in the literature.
Keywords
asymptotic stability; content-addressable storage; neural nets; BAM neural network; bidirectional associative memory neural network; continuous non-monotonic neuron activation function; global asymptotic stability; mixed delays; Artificial neural networks; Associative memory; Asymptotic stability; Circuit stability; Delay; Equations; Stability analysis; BAM; mixed delays; neural networks; stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Apperceiving Computing and Intelligence Analysis (ICACIA), 2010 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-8025-8
Type
conf
DOI
10.1109/ICACIA.2010.5709868
Filename
5709868
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