Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
New convergence behavior of high-order hopfield neural networks with time-varying coefficients
Journal of Computational and Applied Mathematics
Mathematical and Computer Modelling: An International Journal
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
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This paper concerns the existence and exponential stability of periodic solution for the high-order discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. First, we present the criteria for the existence of periodic solution based on the continuation theorem of coincidence degree theory and the Young's inequality, and then we give the criteria for the global exponential stability of periodic solution by using a non-Lyapunov method. After that, we give a numerical example that demonstrates the effectiveness of the theoretical results. The criteria presented in this paper are easy to verify. In addition, the proposed analysis method is easy to extend to other high-order neural networks.