Title :
Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays
Author :
Chen, Boshan ; Wang, Jun
Author_Institution :
Dept. of Math., Hubei Normal Univ., China
Abstract :
In this paper, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.
Keywords :
asymptotic stability; delays; numerical stability; recurrent neural nets; sequential estimation; simulation; time-varying systems; comparison principle; global exponential periodicity; mixed monotone operator; oscillating connection; oscillating parameter; periodic oscillation; recurrent neural network; time-varying delay; Associative memory; Biological system modeling; Cellular neural networks; Delay effects; Differential equations; Neural networks; Neurons; Recurrent neural networks; Stability analysis; Sufficient conditions; Global exponential periodicity; global exponential stability; mixed monotone operator; neural networks; oscillating connections; periodic oscillation; time-varying delay; Animals; Biological Clocks; Computer Simulation; Humans; Models, Neurological; Nerve Net; Neural Networks (Computer); Oscillometry; Periodicity; Signal Processing, Computer-Assisted; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.857953
