This repository contains the code implementation of our NN_2024 paper here.
If you find this repository useful, please cite our paper.
@article{ma2023asynchronous,
title={Non-fragile output-feedback control for time-delay neural networks with persistent dwell time switching: A system mode and time scheduler dual-dependent design},
author={Zhou, Jianping and Ma, Xiaofeng and Yan, Zhilian and Arik, Sabri},
journal={Neural Networks},
volume={169},
pages={733-743},
year={2024},
publisher={Elsevier}}
We use MATLAB as the programming language and employ the Mosek solver along with the YALMIP toolbox as development tools to perform numerical solution and simulation for the proposed system mode and time scheduler dual-dependent design method.
Mosek is a high-performance mathematical optimization solver specifically designed for convex optimization problems. For official documentation, please refer to this link.
YALMIP (Yet Another LMI Parser) is an optimization modeling toolbox for MATLAB that facilitates the formulation and solution of various optimization problems. Developed by Johan Löfberg, YALMIP is primarily used for convex optimization but can also handle certain non-convex problems.
It is important to note that YALMIP itself is not a solver; rather, it serves as a modeling interface that translates user-defined optimization problems into a standard form and then calls external solvers such as Mosek, Gurobi, SDPT3, SeDuMi, and others for solution. For official documentation, please refer to this link.