Coexistence and localμ-stability of multiple equilibrium points for memristive neural networks with nonmonotonic piecewise linear activation functions and unbounded time-varying delays

2016 ◽  
Vol 84 ◽  
pp. 172-180 ◽  
Author(s):  
Xiaobing Nie ◽  
Wei Xing Zheng ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Piecewise Linear ◽  
Equilibrium Points ◽  
Multiple Equilibrium ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Activation Functions ◽  
Multiple Equilibrium Points

scholarly journals Multistability and Multiperiodicity for a General Class of Delayed Cohen-Grossberg Neural Networks with Discontinuous Activation Functions

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yanke Du ◽  
Yanlu Li ◽  
Rui Xu
Keyword(s):  
Neural Networks ◽  
Periodic Orbits ◽  
General Class ◽  
Equilibrium Points ◽  
Sufficient Conditions ◽  
Multiple Equilibrium ◽  
Activation Functions ◽  
Discontinuous Activation ◽  
Discontinuous Activation Functions ◽  
Multiple Equilibrium Points

A general class of Cohen-Grossberg neural networks with time-varying delays, distributed delays, and discontinuous activation functions is investigated. By partitioning the state space, employing analysis approach and Cauchy convergence principle, sufficient conditions are established for the existence and locally exponential stability of multiple equilibrium points and periodic orbits, which ensure thatn-dimensional Cohen-Grossberg neural networks withk-level discontinuous activation functions can haveknequilibrium points orknperiodic orbits. Finally, several examples are given to illustrate the feasibility of the obtained results.

Analysis of Complete Stability for Discrete-Time Cellular Neural Networks with Piecewise Linear Output Functions

2009 ◽  
Vol 21 (5) ◽  
pp. 1434-1458 ◽  
Author(s):  
Xuemei Li
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Piecewise Linear ◽  
Equilibrium Points ◽  
Cellular Neural Networks ◽  
Multiple Equilibrium ◽  
Small Perturbations ◽  
Feedback Matrix ◽  
Multiple Equilibrium Points ◽  
Stability Results

This letter discusses the complete stability of discrete-time cellular neural networks with piecewise linear output functions. Under the assumption of certain symmetry on the feedback matrix, a sufficient condition of complete stability is derived by finite trajectory length. Because the symmetric conditions are not robust, the complete stability of networks may be lost under sufficiently small perturbations. The robust conditions of complete stability are also given for discrete-time cellular neural networks with multiple equilibrium points and a unique equilibrium point. These complete stability results are robust and available.

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