Improved approach to the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks based on new inequalities

This paper studies the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks (FOMVBAMNNs) with general activation functions (AFs). First, the unified model is established for the researched systems of FOMVBAMNNs which can be turned into the corresponding multidimension-valued systems as long as the state variables, the connection weights and the AFs of the neural networks are valued to be real, complex, or quaternion. Then, without any decomposition, the criteria in unified form are derived by constructing the new Lyapunov-Krasovskii functionals (LKFs) in vector form, combining two new inequalities and considering the easy controllers. It is worth mentioning that the obtained criteria have many advantages in higher flexibility, more diversity, smaller computation, and lower conservatism. Finally, a simulation example is provided to illustrate the availability and improvements of the acquired results. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the global Mittag-Leffler synchronization problem for fractional-order multidimension-valued BAM neural networks with general activation functions. The study establishes a unified model for the systems and derives criteria in unified form without decomposition by constructing new Lyapunov-Krasovskii functionals, combining two new inequalities and considering the use of easy controllers. The obtained criteria offer several advantages in terms of higher flexibility, more variety, smaller computation, and lower conservatism.