Finite-time stability criteria for a class of fractional-order neural networks with delay. Issue 3 (April 2016)

• Record Type: Journal Article
• Authors: Chen, Liping; Liu, Cong; Wu, Ranchao; He, Yigang; Chai, Yi
• Is Part Of: Neural computing & applications. Volume 27:Issue 3(2016)
• Page Start: 549
• Page End: 556
• Publication Date: 2016-04
• Subjects: Finite-time stability -- Fractional order -- Neural network -- Delayed systems; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1876-1
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Finite-time stabilities of a class of fractional-order neural networks delayed systems with order $$\alpha {:}$$ α : $$0<\alpha \le 0.5$$ 0 < α ≤ 0.5 and $$0.5<\alpha <1$$ 0.5 < α < 1 are addressed in this paper, respectively. By using inequality technique, two new delay-dependent sufficient conditions ensuring stability of such fractional-order neural networks over a finite-time interval are obtained. Obtained conditions are less conservative than that given in the earlier references. Two numerical examples are given to show the effectiveness of our proposed method.