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To the best of our knowledge, projective synchronization of fractional-order neural networks was previously investigated at the presence of time delay through the use of Laplace transform [33], and no special Lyapunov functions were derived for synchronization analysis.
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Based on impulsive stability theory on delayed dynamical systems, some simple but less conservative criterion are derived for global synchronization of such dynamical network.
By referring to Lyapunov functional method and Kronecker product technique, some sufficient conditions are derived for global synchronization of such systems.
Based on the Lyapunov stabilization theory and Gerschgorin theorem, a simple generic criterion is derived for global synchronization of two coupled chaotic systems with a unidirectional linear error feedback coupling.
Individual connectivity differences derived for the synchronization changes are represented at 3 superior-to-inferior levels through the brain shown in Figure 4 (b d).
Based on the above two discontinuous control methods, comparing with previous continuous control approaches, some less conservative criteria are derived for the finite-time synchronization of the multi-link complex networks.
By designing suitable pinning control schemes, several synchronization criteria are derived for these proposed network models.
Moreover, several synchronization criteria were derived for both delay-independent and delay-dependent asymptotical stability.
Under the framework of Filippov's solution and differential inclusion theory, and by using the adaptive control technique and structuring a novel Lyapunov functional, an adaptive updated law was designed, and two synchronization criteria were derived for memristor-based neural networks with time-varying delay.
Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system.
Especially, a feasible region of the control parameters for each neuron is derived for the realization of finite-time uniform synchronization of the addressed neural networks, which provide a great convenience for the application of the theoretical results.
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