Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks.
basic_science · Level V
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- Record sourced from PubMed, PMID 35576415.
- Also identified by DOI 10.1109/TNNLS.2022.3173620.
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Abstract
In this brief, we consider the stability of inertial memristor-based neural networks with time-varying delays. First, delayed inertial memristor-based neural networks are modeled as continuous systems in the flux-current-voltage-time domain via the mathematical model of Hewlett-Packard (HP) memristor. Then, they are reduced to delayed inertial neural networks with interval parameters uncertainties. Quasi-equilibrium points and quasi-stability are proposed. Quasi-stability criteria of delayed inertial memristor-based neural networks are obtained by matrix measure method, the Halanay inequality, and uncertainty technologies. In the end, a numerical example is provided to show the validity of our results.