A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Su, Huan; Zhao, Yuwei; Li, Wenxue; Ding, Xiaohua
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 2055
• Page End: 2063
• Publication Date: 2016-10
• Subjects: Stochastic BAM neural network -- Time-varying delays -- Exponential stability -- Lyapunov–Krasovskii functional -- Graph theory -- 34K45 -- 37N35 -- 68M10; 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/s00521-015-2005-x
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This paper investigates the global exponential stability for a stochastic bidirectional associative memory (BAM) neural network with time-varying delays. Based on the principle of graph theory, a new method forp th moment exponential stability is derived by combining some inequalities, Lyapunov method and stochastic analysis. The obtained criteria have close relations to the topology property of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the theoretical results.