Abstract
This paper considers projective synchronization of fractional-order delayed neural networks. Sufficient conditions for projective synchronization of master–slave systems are achieved by constructing a Lyapunov function, employing a fractional inequality and the comparison principle of linear fractional equation with delay. The corresponding numerical simulations demonstrate the feasibility of the theoretical result.
Highlights
Neural networks have attracted great attention due to their wide applications, including the signal processing, parallel computation, optimization, and artificial intelligence
It is well known that fractional calculus is the generalization of integer-order calculus to arbitrary order
The existence of infinite memory can help fractional-order models better describe the system’s dynamical behaviors as illustrated in [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Taking these factors into consideration, fractional calculus was introduced to neural networks forming fractional-order neural networks, and some interesting results on synchronization were demonstrated [24,25,26,27,28,29]
Summary
Neural networks have attracted great attention due to their wide applications, including the signal processing, parallel computation, optimization, and artificial intelligence. Some results with respect to projective synchronization of fractional-order neural networks were considered [30,31,32]. In [30], projective synchronization for fractional neural networks was studied.
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