Global stability of a class of discrete-time recurrent neural networks
IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications - Tập 49 Số 8 - Trang 1104-1117 - 2002
Tóm tắt
This paper presents several analytical results on global asymptotic stability (GAS) and global exponential stability (GES) for the equilibrium states of a general class of discrete-time recurrent neural networks (DTRNNS) with asymmetric connection weight matrices and globally Lipschitz continuous and monotone nondecreasing activation functions. A necessary and sufficient condition is formulated to guarantee the existence and uniqueness of equilibria of such DTRNNS. The obtained results are less restrictive, different from, and improve upon the existing ones on GAS and GES of neural networks with special classes of activation functions.
Từ khóa
#Recurrent neural networks #Neural networks #Asymptotic stability #Lyapunov method #Symmetric matrices #Stability analysis #Sufficient conditions #Signal processing #Stability criteria #Design optimizationTài liệu tham khảo
khalil, 1992, Nonlinear Systems
michel, 1977, Qualitative Analysis of Large Scale Dynamical Systems
10.1109/72.914529
10.1109/9.917666
passino, 1998, Stability Analysis of Discrete Event Systems
10.1017/CBO9780511810817
10.1109/81.668873
10.1109/81.873882
10.1109/81.668877
10.1109/ICNN.1993.298717
jin, 0, equilibrium stability of discrete-time dynamical neural model, Proc 1993 World Congr Neural Networks 1993, 4, 276
10.1109/72.329692
10.1109/72.508944
10.1016/0893-6080(92)90011-7
10.1109/81.199861
10.1109/72.134288
10.1109/81.298364
10.1109/72.501720
10.1109/72.207617
10.1109/31.68298
10.1109/10.52325
10.1109/81.401145
10.1109/72.883412
10.1109/81.404065
10.1137/0614036
10.1073/pnas.81.10.3088
10.1109/81.641813
10.1073/pnas.79.8.2554
10.1109/72.182701
10.1109/IJCNN.1992.227277
10.1109/72.105420
10.1109/72.809094
10.1109/72.80289
10.1109/72.728385
10.1109/72.508941