Title :
Existence and global exponential stability of anti-periodic solution of higher-order recurrent neural networks with distributed delays and impulse on time scales
Author_Institution :
Dept. of Stat. & Math., Yunnan Univ. of Finances & Econ., Kunming, China
Abstract :
By using the continuation theorem of coincidence degree theory, M-matrix theory and constructing some suitable Lyapunov functions, some sufficient conditions are obtained for the existence and global exponential stability of anti-periodic solutions of higher-order recurrent type neural networks with distributed delays and impulses on time scales without assuming the boundedness of the activation functions fj, gj.
Keywords :
Lyapunov methods; asymptotic stability; delays; distributed control; matrix algebra; recurrent neural nets; transfer functions; Lyapunov functions; M-matrix theory; activation functions; antiperiodic solution; coincidence degree theory; continuation theorem; distributed delays; distributed impulses; global exponential stability; higher-order recurrent neural networks; time scales; Biological neural networks; Delay; Equations; Kernel; Lyapunov methods; Neurons; Functional difference equations; positive periodic solutions; strict-set-contraction;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4577-1414-6
DOI :
10.1109/CECNet.2012.6201594
