Title :
Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays
Author :
Jinde Cao ; Kun Yuan ; Han-Xiong Li
Author_Institution :
Dept. of Math., Southeast Univ., Nanjing
Abstract :
By employing the Lyapunov-Krasovskii functional and linear matrix inequality (LMI) approach, the problem of global asymptotical stability is studied for recurrent neural networks with both discrete time-varying delays and distributed time-varying delays. Some sufficient conditions are given for checking the global asymptotical stability of recurrent neural networks with mixed time-varying delay. The proposed LMI result is computationally efficient as it can be solved numerically using standard commercial software. Two examples are given to show the usefulness of the results
Keywords :
Lyapunov methods; asymptotic stability; delay systems; linear matrix inequalities; recurrent neural nets; time-varying systems; Lyapunov-Krasovskii functional; discrete time-varying delays; distributed time-varying delays; global asymptotical stability; linear matrix inequality approach; recurrent neural networks; Asymptotic stability; Bifurcation; Chaos; Delay effects; Linear matrix inequalities; Neural networks; Neurons; Recurrent neural networks; Software standards; Sufficient conditions; Discrete delays; distributed delays; global asymptotical stability; linear matrix inequality (LMI); recurrent neural networks (RNNs); time-varying delays; Algorithms; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.881488
