DocumentCode :
1370005
Title :
An unpredictable-dynamics approach to neural intelligence
Author :
Zak, Michail
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA
Volume :
6
Issue :
4
fYear :
1991
Firstpage :
4
Lastpage :
10
Abstract :
The theoretical basis for a dynamic neural network architecture that takes advantage of the notion of terminal chaos to process information in a way that is phenomenologically similar to brain activity is presented. The architecture exploits the phenomenology of nonlinear dynamic systems as an alternative to the traditional paradigm of finite-state machines. It is based on some effects of nonLipschitzian dynamics. The nonlinear phenomenon of terminal chaos and its relevance to brain activity are examined.<>
Keywords :
neural nets; nonlinear systems; brain activity; dynamic neural network architecture; finite-state machines; neural intelligence; nonLipschitzian dynamics; nonlinear dynamic systems; terminal chaos; unpredictable-dynamics approach; Adaptive systems; Artificial intelligence; Artificial neural networks; Biological system modeling; Biological systems; Brain; Chaos; Information processing; Neurons; Propulsion;
fLanguage :
English
Journal_Title :
IEEE Expert
Publisher :
ieee
ISSN :
0885-9000
Type :
jour
DOI :
10.1109/64.85916
Filename :
85916
Link To Document :
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