H∞ stabilization problem for memristive neural networks with time-varying delays

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.

$ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ control for memristive NNs with non-necessarily differentiable time-varying delay

This paper investigates $ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ control for memristive neural networks (MNNs) with a non-necessarily differentiable time-varying delay. The objective is to design an output-feedback controller to ensure the $ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ stability of the considered MNN. A criterion on the $ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ stability is proposed using a Lyapunov functional, the Bessel-Legendre inequality, and the convex combination inequality. Then, a linear matrix inequalities-based design scheme for the required output-feedback controller is developed by decoupling nonlinear terms. Finally, two examples are presented to verify the proposed $ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ stability criterion and design method.