LMI-Based Approach for Global Asymptotic Stability Analysis of Recurrent Neural Networks with Various Delays and Structures
- 23 May 2011
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 22 (7), 1032-1045
- https://doi.org/10.1109/tnn.2011.2131679
Abstract
Global asymptotic stability problem is studied for a class of recurrent neural networks with distributed delays satisfying Lebesgue-Stieljies measures on the basis of linear matrix inequality. The concerned network model includes many neural network models with various delays and structures as its special cases, such as the delays covering the discrete delays and distributed delays, and the network structures containing the neutral-type networks and high-order networks. Therefore, many new stability criteria for the above neural network models have also been derived from the present stability analysis method. All the obtained stability results have similar matrix inequality structures and can be easily checked. Three numerical examples are used to show the effectiveness of the obtained results.Keywords
This publication has 61 references indexed in Scilit:
- Identification of quadratic systems using higher order cumulants and neural networks: Application to model the delay of video-packets transmissionApplied Soft Computing, 2011
- A Hybrid Higher Order Neural Classifier for handling classification problemsExpert Systems with Applications, 2011
- Robust global synchronization of complex networks with neutral-type delayed nodesApplied Mathematics and Computation, 2010
- Global exponential stability results for neutral-type impulsive neural networksNonlinear Analysis: Real World Applications, 2010
- Exponential stability of impulsive high-order cellular neural networks with time-varying delaysNonlinear Analysis: Real World Applications, 2010
- New results for global stability of a class of neutral-type neural systems with time delaysApplied Mathematics and Computation, 2009
- Global exponential stability of high order recurrent neural network with time-varying delaysApplied Mathematical Modelling, 2009
- ANALYSIS ON GLOBAL STABILITY OF STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPEModern Physics Letters B, 2008
- An LMI approach to stability analysis of stochastic high-order Markovian jumping neural networks with mixed time delaysNonlinear Analysis: Hybrid Systems, 2008
- Novel global stability criteria for high-order Hopfield-type neural networks with time-varying delaysJournal of Mathematical Analysis and Applications, 2007