Abstract
This paper deals with the finite-time stochastic synchronization for a class of memristorbased bidirectional associative memory neural networks (MBAMNNs) with time-varying delays and stochastic disturbances. Firstly, based on the physical property of memristor and the circuit of MBAMNNs, a MBAMNNs model with more reasonable switching conditions is established. Then, based on the theory of Filippov’s solution, by using Lyapunov–Krasovskii functionals and stochastic analysis technique, a sufficient condition is given to ensure the finite-time stochastic synchronization of MBAMNNs with a certain controller. Next, by a further discussion, an errordependent switching controller is given to shorten the stochastic settling time. Finally, numerical simulations are carried out to illustrate the effectiveness of theoretical results.
Highlights
In 1971, Chua postulated a new kind of passive circuit element called memristor, which can connect the charge and magnetic flux [7]
Bidirectional associative memory neural networks (BAMNNs) were proposed in 1988 [15]. They display a two-way associative search for stored bipolar vector pairs and generalize the single-layer autoassociative Hebbian correlation to a two-layer patternmatched heteroassociative circuits [37]. This kind of neural networks have been successfully applied in various fields, including automatic control, pattern recognition and associative memory
Motivated by the discussion above, in this paper, we study the finite-time synchronization of memristorbased bidirectional associative memory neural networks (MBAMNNs) with time-varying delays and stochastic disturbances
Summary
In 1971, Chua postulated a new kind of passive circuit element called memristor, which can connect the charge and magnetic flux [7]. A switching control for finite-time synchronization of memristor-based BAM neural networks. Bidirectional associative memory neural networks (BAMNNs) were proposed in 1988 [15] They display a two-way associative search for stored bipolar vector pairs and generalize the single-layer autoassociative Hebbian correlation to a two-layer patternmatched heteroassociative circuits [37]. This kind of neural networks have been successfully applied in various fields, including automatic control, pattern recognition and associative memory. Motivated by the discussion above, in this paper, we study the finite-time synchronization of MBAMNNs with time-varying delays and stochastic disturbances.
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