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Synchronisation of chaotic neural networks with unknown parameters and random time-varying delays based on adaptive sampled-data control and parameter identification

Synchronisation of chaotic neural networks with unknown parameters and random time-varying delays based on adaptive sampled-data control and parameter identification

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This study investigates the synchronisation problem of chaotic neural networks with unknown parameters and random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the neural networks with random time-varying delays is transformed into one with deterministic varying delays and stochastic parameters. A simple and robust adaptive sampled-data controller is designed such that the response system can be synchronised with a drive system with unknown parameters by using suitable parameter identification and the Lyapunov stability theory. The proposed synchronisation criteria are easily verified and do not need to solve any linear matrix inequality. Numerical simulations are carried out to demonstrate the effectiveness of the established synchronisation laws.

Inspec keywords: linear matrix inequalities; parameter estimation; synchronisation; Lyapunov methods; time-varying systems; stochastic processes; delays; neural nets; adaptive control; robust control

Other keywords: stochastic variable; deterministic varying delays; robust adaptive sampled-data controller; Bernoulli distribution; synchronisation problem; linear matrix inequality; parameter identification; chaotic neural networks; unknown parameters; Lyapunov stability theory; random time-varying delays

Subjects: Time-varying control systems; Distributed parameter control systems; Linear algebra (numerical analysis); Other topics in statistics; Self-adjusting control systems; Stability in control theory; Neural nets (theory)

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