In this paper, we study the problem of finite-time stability and passivity criteria for discrete-time neural networks (DNNs) with variable delays. The main objective is how to effectively evaluate the finite-time passivity conditions for NNs. To achieve this, some new weighted summation inequalities are proposed for application to a finite-sum term appearing in the forward difference of a novel Lyapunov-Krasovskii functional, which helps to ensure that the considered delayed DNN is passive. The derived passivity criteria are presented in terms of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed results.
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Accepted/In press - 2018 May 22|
- Artificial neural networks
- Asymptotic stability
- Circuit stability
- Discrete-time neural networks (DNNs)
- finite-time passivity (FTP) analysis
- Lyapunov method
- Numerical stability
- Stability criteria
- weighted summation inequality.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence