Global exponential periodicity and global exponential stability of a class of recurrent neural networks with various activation functions and time-varying delays

Neural Networks

Volumn 20, Issue 10, 2007, Pages 1067-1080

  Chen, Boshan ^a   Wang, Jun ^b  

^a HUBEI NORMAL UNIVERSITY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Comparison principle; Global exponential stability; Mixed monotone operator; Periodic oscillation; Recurrent neural network

Indexed keywords

ALGEBRA; ASYMPTOTIC STABILITY; CONVERGENCE OF NUMERICAL METHODS; MATHEMATICAL OPERATORS; TIME DELAY;

ACTIVATION FUNCTIONS; COMPARISON PRINCIPLE; MIXED MONOTONE OPERATORS; PERIODIC OSCILLATION;

RECURRENT NEURAL NETWORKS;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; MATHEMATICAL MODEL; OSCILLATION; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; TIME;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; NERVE NET; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PERIODICITY; REACTION TIME; TIME FACTORS;

EID: 36549005113     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2007.07.007     Document Type: Article

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