Abstract
In this article, we discuss anti-periodic oscillations of BAM neural networks with leakage delays. A sufficient criterion guaranteeing the existence and exponential stability of the involved model is presented by utilizing mathematic analysis methods and Lyapunov ideas. The theoretical results of this article are novel and are a key supplement to some earlier studies.
Highlights
In the past several decades, the dynamics of BAM neural networks has been widely investigated for their essential applications in classification, pattern recognition, optimization, signal and image processing, and so on [1–41]
Since the delays in neural networks are usually time-varying in the real world, Liu [49] discussed the global exponential stability for the following general
We show that x∗(t) is a T -anti-periodic solution of (1.4)
Summary
In the past several decades, the dynamics of BAM neural networks has been widely investigated for their essential applications in classification, pattern recognition, optimization, signal and image processing, and so on [1–41]. Since the delays in neural networks are usually time-varying in the real world, Liu [49] discussed the global exponential stability for the following general There have been rare reports on the existence and exponential stability of anti-periodic solutions of neural networks, especially for neural networks with leakage delays. We think that the investigation on the existence and stability of anti-periodic solutions for neural networks with leakage delays has significant value.
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