Abstract
In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results.
Highlights
Associative memory is one of the most important activities of human brains
MAMNNs are the extension of bidirectional associative memory neural networks(BAMNNs), and they are similar in structure, i.e. there is no connection between the neurons in the same field, but there exist interconnections between the neurons from different fields
Motivated by the above discussions, the main contributions of this paper can be summarized in the following: 1. We propose a novel model of MMAMNNs, which considers time-varying delays in leakage terms via sampled-data control
Summary
Associative memory is one of the most important activities of human brains. It includes oneto-many association, many-to-one association and many-to-many association. The global exponential stability of MAMNNs with time-varying delays were analyzed in [3]. As an extension of MBAMNNs, the study of memristive multidirectional associative memory neural networks(MMAMNNs) have attracted the attention of researchers [18]. The problem of exponential stability for switched MNNs with time-varying delays was studied in [22]. We propose a novel model of MMAMNNs, which considers time-varying delays in leakage terms via sampled-data control. Stability of memristive multidirectional associative memory neural networks the characters of both MAMNNs and MNNs, which can simulate the associative memory process of human brains more effectively. The model of MMAMNNs with time-varying delays in leakage terms via sampled-data control are proposed and some preliminaries are introduced.
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