Design and Practical Stability of a New Class of Impulsive Fractional-Like Neural Networks

Abstract

In this paper, a new class of impulsive neural networks with fractional-like derivatives is defined, and the practical stability properties of the solutions are investigated. The stability analysis exploits a new type of Lyapunov-like functions and their derivatives. Furthermore, the obtained results are applied to a bidirectional associative memory (BAM) neural network model with fractional-like derivatives. Some new results for the introduced neural network models with uncertain values of the parameters are also obtained.

Keywords

• neural networks
• fractional-like derivative
• impulses
• practical stability
• <i>h</i>−manifolds