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This paper is concerned with the global asymptotic stability of the neutral type fractional‐order memristor‐based neural networks with leakage discrete and distributed delays. By building a sensible Lyapunov fractional related with integral and fractional derivative terms, a few satisfactory conditions comprehensively asymptotic stability is gotten. At that point, the global asymptotically stability investigation of fractional‐order neural systems utilizes the results from the obtained LMI conditions. Finally, numerical examples are provided to illustrate the effectiveness of the established of the proposed results.

In this paper synchronization of fractional order fuzzy BAM neural networks with time varying delays and reaction diffusion terms is studied. The time varying delays consist of discrete delays and distributed delays are considered. Then, some sufficient conditions black are presented to guarantee the global asymptotic stability of the error system by using Lyapunov-Krasovskii functional having the double integral terms, we utilized Jensens inequality techniques and LMI approach. Accordingly, we accomplished synchronization of master-slave fuzzy BAMNNs. The delay dependent stability conditions are set up in terms of linear matrix inequalities(LMIs), which can be productively understood utilizing Matlab LMI control tool box. At last, illustrative numerical results have been provided to verify the correctness and effectiveness of the obtained results. INDEX TERMS Synchronization,Time varying delays, Reaction Diffusion.

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.

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