Attractivity Analysis of Memristor-Based Cellular Neural Networks With Time-Varying Delays
Top Cited Papers
- 18 September 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 25 (4), 704-717
- https://doi.org/10.1109/tnnls.2013.2280556
Abstract
This paper presents new theoretical results on the invariance and attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2 n to 2 2n2 +n (2 2n2 times) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail.Keywords
Funding Information
- Research Grants Council of the Hong Kong Special Administrative Region
- Hong Kong Scholars Program
- National Natural Science Foundation of China (11101133)
This publication has 58 references indexed in Scilit:
- Global anti-synchronization of a class of chaotic memristive neural networks with time-varying delaysNeural Networks, 2013
- Analysis and design of winner-take-all behavior based on a novel memristive neural networkNeural Computing & Applications, 2013
- Exponential synchronization of coupled memristive neural networks with time delaysNeural Computing & Applications, 2013
- Anti-synchronization control of a class of memristive recurrent neural networksCommunications in Nonlinear Science and Numerical Simulation, 2013
- Associative Learning of Integrate-and-Fire Neurons with Memristor-Based SynapsesNeural Processing Letters, 2012
- Exponential Stabilization of Memristive Neural Networks With Time DelaysIEEE Transactions on Neural Networks and Learning Systems, 2012
- Dynamic behaviors of memristor-based delayed recurrent networksNeural Computing & Applications, 2012
- Multistability of periodic delayed recurrent neural network with memristorsNeural Computing & Applications, 2012
- Synchronization control of a class of memristor-based recurrent neural networksInformation Sciences, 2012
- Dynamics Analysis of a Class of Memristor-Based Recurrent Networks with Time-Varying Delays in the Presence of Strong External StimuliNeural Processing Letters, 2011