DocumentCode :
2776779
Title :
Dynamic behaviors of hybrid Lotka-Volterra recurrent neural networks with memristor characteristics
Author :
Wu, Ailong ; Zeng, Zhigang
Author_Institution :
Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, a general class of hybrid Lotka-Volterra recurrent neural networks with memristor characteristics is formulated and studied. Some sufficient conditions on nondivergence, global attractivity and complete stability of the network are obtained, respectively. These results can be applied to the memristive dynamic memories. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. These theoretical analysis can characterize the fundamental electrical properties of memristor devices and provide convenience for applications. A numerical example is given to illustrate the theoretical findings via computer simulations.
Keywords :
differential equations; memristors; recurrent neural nets; computer simulations; differential equations; discontinuous right-hand side; dynamic behaviors; electrical properties; hybrid Lotka-Volterra recurrent neural networks; memristive dynamic memories; memristor characteristics; memristor devices; network complete stability; network global attractivity; network nondivergence; Fires; Sensor arrays; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252746
Filename :
6252746
Link To Document :
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