An introduction to difference equations
An introduction to difference equations
Cellular Neural Networks and Visual Computing
Cellular Neural Networks and Visual Computing
Globally asymptotical stability of discrete-time analog neural networks
IEEE Transactions on Neural Networks
Some extensions of a new method to analyze complete stability of neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Multiperiodicity of Discrete-Time Delayed Neural Networks Evoked by Periodic External Inputs
IEEE Transactions on Neural Networks
Stability Analysis and the Stabilization of a Class of Discrete-Time Dynamic Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
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.