Multistability of recurrent neural networks with time-varying delays and the piecewise linear activation function.
In this brief, stability of multiple equilibria of recurrent neural networks with time-varying delays and the piecewise linear activation function is studied. A sufficient condition is obtained to ensure that n-neuron recurrent neural networks can have (4k - 1)(n) equilibrium points and (2k)(n) of them are locally exponentially stable. This condition improves and extends the existing stability results in the literature. Simulation results are also discussed in one illustrative example.
Zeng
Zhigang
Z
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China. zgzeng@mail.hust.edu.cn
Huang
Tingwen
T
Zheng
Wei Xing
WX
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D006801:Humans; D016014:Linear Models; D055641:Mathematical Concepts; D008568:Memory / Q000502:physiology; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D013997:Time Factors