Global polynomial periodicity and polynomial stability of proportional delay Cohen-Grossberg neural networks

Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov-Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically.

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with proportional delays

The issue of fixed-time stabilization (FTS) for a class of fuzzy neutral-type inertial neural networks (FNTINNs) with proportional delays (PDs) is discussed in this research. Two feedback controllers were constructed utilizing the fixed-time stability theorem. Two criteria for figuring out the FTS of FNTINNs with PDs were acquired by building suitable Lyapunov functions (LFs). The theoretical conclusions offered could potentially contribute to a more precise calculation of the upper settling-time (ST) when compared to previously conducted research. Two simulation examples are provided to demonstrate the applicability of the stated theoretical conclusions.