Abstract
In this paper, we discuss the driving-response synchronization problem for two memristive neural networks with retarded and advanced arguments under the condition of additional noise. The control law is related to the linear time-delay feedback term, and the discontinuous feedback term. Moreover, the random different equation is used to prove the stability of this theory. At the end, the simulation results verify the correctness of the theoretical results.
Highlights
In the past 10 years, neural networks have shown great potential application in pattern classification, associative memory
The linearity of the feedback term has been considering in control, and the discontinuous feedback term is proposing to ensure the global synchronization of the two memristive neural network (MNN)
In the mean square RMNN (3) and (4) can achieve the global asymptotic synchronization if there is a matrix R that is positive diagonal matrix, R = diag{r1, r2, rn}, and positionally of definientia matrix P = pij n×n, O = oij n×n, it is shown in this equation:
Summary
In the past 10 years, neural networks have shown great potential application in pattern classification, associative memory. The linearity of the feedback term has been considering in control, and the discontinuous feedback term is proposing to ensure the global synchronization of the two MNNs. Recently, in [12] [13] [14] DMNN has provoked considerable attention for the sake of both theoretical interest and practical applications. Numerical theoretical analysis and simulated experiments have demonstrated that MNN can possess more computation power and information capacity, which would significantly broaden the application of neural networks in information processing, associative memory and pattern recognition. We continue to discuss the master-slave synchronization of memristor neural network (MNN) with retarded and advanced argument.
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