Synchronization problem for time-delay recurrent neural networks*

2009 ◽  
Vol 42 (14) ◽  
pp. 426-430 ◽  
Author(s):  
Daniela Danciu ◽  
Cosmin Ionete
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Synchronization Problem

Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

scholarly journals SYNCHRONIZATION BETWEEN INTEGER AND FRACTIONAL ORDER DELAY NEURAL NETWORKS SYSTEM

2020 ◽  
Vol 2 (2) ◽  
pp. 121-126
Author(s):  
Fatin Nabila Abd Latiff ◽  
Wan Ainun Mior Othman
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Recurrent Neural Networks ◽  
Secure Communication ◽  
Sliding Mode ◽  
Mode Control ◽  
Synchronization Problem ◽  
Active Sliding Mode Control ◽  
Fractional Method

The purpose is to present a method for synchronizing a recurrent neural networks system between integer and fractional-order order delay by active sliding mode control . The Active Sliding Mode Control (ASMC) scheme is used to solve the synchronization problem between the integer-order delayed recurrent neural networks system via active sliding mode control (IoDRNNASM) systems and the fractional-order delay recurrent neural networks system via active sliding mode control (FoDRNNASM) system based on the Lyapunov direct fractional method (LDFM). To explore the behavior of the IoDRNNASM systems and the FoDRNNASM systems, we performed the technique of numerical simulations using MATLAB software to prove the feasibility and strength of the archived outcomes. This concept can also be enhanced with the implementation of double encryption using RSA encryption to secure communication. Because we expected in the future that this enhanced concept will strengthen and increase the network security capabilities that will provide powerful protection in secure communications.

ROBUST SYNCHRONIZATION CRITERIA FOR RECURRENT NEURAL NETWORKS VIA LINEAR FEEDBACK

2007 ◽  
Vol 17 (08) ◽  
pp. 2723-2738 ◽  
Author(s):  
XIA HUANG ◽  
JAMES LAM ◽  
JINDE CAO ◽  
SHENGYUAN XU
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Sufficient Conditions ◽  
Linear Feedback ◽  
Linear Matrix ◽  
Time Varying Delay ◽  
Synchronization Problem ◽  
Robust Synchronization ◽  
Control Robustness ◽  
Delay Dependent

In this paper, the robust synchronization problem is addressed for recurrent neural networks with time-varying delay by linear feedback control. Robustness in the present paper is referred to as the allowance of parameters mismatch between the drive system and the response system. Sufficient conditions for robust synchronization with a synchronization error bound, expressed as linear matrix inequality (LMI), are derived based on Lyapunov–Krasovskii functionals. Both delay-dependent and delay-independent conditions are obtained. Two examples are given to illustrate the results.

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