New Results on Robust Finite-Time Extended Dissipativity for Uncertain Fractional-Order Neural Networks

This article investigates the problem of finite-time extended dissipativity performance for fractional-order neural networks (FONNs) with uncertainties. We first extend the concept of finite-time extended dissipativity of integer-order systems to fractional-order systems ones. By exploiting the introduced definition combined with some auxiliary properties of fractional calculus, linear matrix inequalities (LMIs) techniques, and the finite-time stability theory, some order-dependent sufficient conditions are presented to ensure the extended dissipativity performance of the concerned class of FONNs with uncertainties during a prescribed time interval. The obtained conditions are derived in terms of the tractable LMIs which can be effectively solved by various computational tools. Two numerical examples are given to demonstrate the correctness of the proposed theoretical results.