Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays. (English) Zbl 1395.37056
Summary: In recent years, complex-valued recurrent neural networks have been developed and analysed in-depth in view of that they have good modelling performance for some applications involving complex-valued elements. In implementing continuous-time dynamical systems for simulation or computational purposes, it is quite necessary to utilize a discrete-time model which is an analogue of the continuous-time system. In this paper, we analyse a discrete-time complex-valued recurrent neural network model and obtain the sufficient conditions on its global exponential periodicity and exponential stability. Simulation results of several numerical examples are delineated to illustrate the theoretical results and an application on associative memory is also given.
MSC:
| 37N25 | Dynamical systems in biology |
| 92C20 | Neural biology |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
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