Abstract
This article examines the drive-response synchronization of a class of fractional order uncertain BAM (Bidirectional Associative Memory) competitive neural networks. By using the differential inclusions theory, and constructing a proper Lyapunov-Krasovskii functional, novel sufficient conditions are obtained to achieve global asymptotic stability of fractional order uncertain BAM competitive neural networks. This novel approach is based on the linear matrix inequality (LMI) technique and the derived conditions are easy to verify via the LMI toolbox. Moreover, numerical examples are presented to show the feasibility and effectiveness of the theoretical results.
Highlights
Academic Editor: Ivanka Stamova, In recent years, fractional calculus has attracted the attention of researchers because it can describe real phenomenon more accurately
We will present the synchronization of following fractional order uncertain BAM competitive neural networks with delays
This article investigated the stability of fractional order uncertain BAM competitive neural networks
Summary
Academic Editor: Ivanka Stamova, In recent years, fractional calculus has attracted the attention of researchers because it can describe real phenomenon more accurately. The study of fractional-order synchronization has been attracting a host of attention due to emerging excellent applications in biological technology, chemical systems, ecological systems, cryptography, etc. Much attention has been devoted to analyzing the stability and synchronization of Competitive neural networks, for example [23,24]. Since fractional calculus provides a better way to show the nature of hereditary and memory in dynamical process, fractional order competitive neural network (FCNN) is more appropriate than integer-order CNN. The multi-stability, global stability, and complete synchronization for fractional-order competitive neural network are researched [25,26]. We propose stability and synchronization criteria for the fractional order uncertain BAM competitive neural networks. Numerical results are given to show the effectiveness of the proposed results
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