Stability (learning theory)

Combinatorics

Physics

Nonlinear system

Topology (electrical circuits)

Uniqueness

Mathematics

Mathematical proof

Machine learning

Mathematical analysis

Artificial intelligence

Control (management)

Contraction principle

Geometry

Synchronization (alternating current)

Computer science

Quantum mechanics

Differential equation

Applied mathematics

Equilibrium point

Control theory (sociology)

Artificial neural network

Lyapunov function

International Journal of Control, Automation, & Systems

In this article, we explore the theoretical issues on the drive-response synchronization of a class of fractional order uncertain complex valued neural networks (FOUCNNs) with mixed time varying delays and impulses. Based upon the contraction mapping principle, robust analysis techniques, as well as Riemann-Liouville (R-L) derivative, we derive a new set of novel sufficient conditions for the existence and uniqueness of equilibrium point of such neural network system, while by applying the Lyapunov functional approach, the global stability of the equilibrium solutions are obtained. Furthermore, the synchronization criterion of FOUCNNs is also attracted by means of the adaptive error feedback control strategy. Finally, two examples are provided along with the simulation results to demonstrate the effectiveness of our main proofs.

Global Robust Synchronization of Fractional Order Complex Valued Neural Networks with Mixed Time Varying Delays and Impulses