Finite-time stabilization for fractional-order inertial neural networks with time varying delays
Chaouki Aouiti, Jinde Cao, Hediene Jallouli, Chuangxia Huang
Abstract
This paper deals with the finite-time stabilization of fractional-order inertial neural network with varying time-delays (FOINNs). Firstly, by correctly selected variable substitution, the system is transformed into a first-order fractional differential equation. Secondly, by building Lyapunov functionalities and using analytical techniques, as well as new control algorithms (which include the delay-dependent and delay-free controller), novel and effective criteria are established to attain the finite-time stabilization of the addressed system. Finally, two examples are used to illustrate the effectiveness and feasibility of the obtained results.
Topics & Concepts
Control theory (sociology)Artificial neural networkInertial frame of referenceController (irrigation)Computer scienceVariable (mathematics)Order (exchange)Lyapunov functionControl (management)MathematicsNonlinear systemMathematical analysisArtificial intelligencePhysicsAgronomyEconomicsBiologyQuantum mechanicsFinanceNeural Networks Stability and SynchronizationNeural Networks and ApplicationsStability and Controllability of Differential Equations