Abstract
In this paper, we offer an approach about the dissipativity of neutral-type memristive neural networks (MNNs) with leakage, additive time, and distributed delays. By applying a suitable Lyapunov–Krasovskii functional (LKF), some integral inequality techniques, linear matrix inequalities (LMIs) and free-weighting matrix method, some new sufficient conditions are derived to ensure the dissipativity of the aforementioned MNNs. Furthermore, the global exponential attractive and positive invariant sets are also presented. Finally, a numerical simulation is given to illustrate the effectiveness of our results.
Highlights
In the recent decades, neural networks have been widely applied in many areas, such as automatic control engineering, image processing, associative memory, pattern recognition, parallel computing, and so on [1, 2]
Some researchers have investigated the global dissipativity of neural networks with mixed delays, by giving some sufficient conditions to obtain the global exponentially attracting sets [25, 26]
To the best of our knowledge, few studies have considered the dissipativity of neutral-type memristive neural networks with mixed delays
Summary
Neural networks have been widely applied in many areas, such as automatic control engineering, image processing, associative memory, pattern recognition, parallel computing, and so on [1, 2]. For the dissipativity analysis of neural networks, it is essential to find global exponentially attracting sets. Some researchers have investigated the global dissipativity of neural networks with mixed delays, by giving some sufficient conditions to obtain the global exponentially attracting sets [25, 26].
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