Finance

Stability (learning theory)

Electrical engineering

Uniqueness

Mathematics

Laplace transform

Machine learning

Memristor

Engineering

Mathematical analysis

Order (exchange)

Economics

Set (abstract data type)

Programming language

Gronwall's inequality

Computer science

Inequality

Differential equation

Applied mathematics

Equilibrium point

Artificial neural network

Differential inclusion

IEEE Transactions on Cybernetics

This paper studies one type of delayed memristor-based fractional-order neural networks (MFNNs) on the finite-time stability problem. By using the method of iteration, contracting mapping principle, the theory of differential inclusion, and set-valued mapping, a new criterion for the existence and uniqueness of the equilibrium point which is stable in finite time of considered MFNNs is established when the order α satisfies 0 <; α <; 1. Then, when 1 <; α <; 2, on the basis of generalized Gronwall inequality and Laplace transform, a sufficient condition ensuring the considered MFNNs stable in finite time is given. Ultimately, simulation examples are proposed to demonstrate the validity of the results.

Finite-Time Stability of Delayed Memristor-Based Fractional-Order Neural Networks