Dynamics of a class of discete-time neural networks and their comtinuous-time counterparts
Mathematics and Computers in Simulation
Global Robust Exponential Stability of Interval Neural Networks with Delays
Neural Processing Letters
On Robust Exponential Periodicity of Interval Neural Networks with Delays
Neural Processing Letters
Global Robust Exponential Stability of Interval General BAM Neural Network with Delays
Neural Processing Letters
Robust stability for interval Hopfield neural networks with time delay
IEEE Transactions on Neural Networks
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
Unsupervised learning in noise
IEEE Transactions on Neural Networks
Bidirectional associative memories: Different approaches
ACM Computing Surveys (CSUR)
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In this paper, we first investigate the existence of a periodic solution to interval general bidirectional associative memory (BAM) neural networks with multiple delays on time scales by the continuation theorem of coincidence degree theory. Then, by constructing a Lyapunov functional, we discuss the global exponential stability of the periodic solution for such neural networks on time scales. The paper unifies periodic discrete-time and continuous-time BAM neural networks under the same framework.