Abstract
The discrete‐time delayed neural network with complex‐valued linear threshold neurons is considered. By constructing appropriate Lyapunov‐Krasovskii functionals and employing linear matrix inequality technique and analysis method, several new delay‐dependent criteria for checking the boundedness and global exponential stability are established. Illustrated examples are also given to show the effectiveness and less conservatism of the proposed criteria.
Highlights
In the past decade, neural networks have received increasing interest owing to their applications in many areas such as signal processing, pattern recognition, associative memories, parallel computation, and optimization solvers 1
During the implementation on very large-scale integrated chips, transmitting time delays will destroy the dynamical behaviors of neural networks
Some important results on the boundedness, convergence, global exponential stability, synchronization, state estimation, Discrete Dynamics in Nature and Society and passivity analysis have been reported for delayed neural networks; see 1–9 and the references theorems for some recent publications
Summary
Neural networks have received increasing interest owing to their applications in many areas such as signal processing, pattern recognition, associative memories, parallel computation, and optimization solvers 1 In such applications, the qualitative analysis of the dynamical behaviors is a necessary step for the practical design of neural networks 2. Authors considered a class of discrete time recurrent neural networks with complex-valued weights and activation function 22, 23. In 23 , the boundedness, global attractivity, and complete stability were investigated for discrete-time recurrent neural networks with complex-valued linear threshold neurons. Motivated by the above discussions, the objective of this paper is to study the problem on boundedness and stability of discrete-time delayed neural network with complex-valued linear threshold neurons
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