Stability Analysis of Time-varying Delay Neural Network for Convex Quadratic Programming With Equality Constraints and Inequality Constraints

TL;DR

This paper introduces a time-varying delay neural network designed for solving convex quadratic programming problems with constraints, demonstrating its stability and effectiveness through theoretical proofs and numerical examples.

Contribution

It proposes a novel neural network model with time-varying delays that is more concise and guarantees global exponential stability for constrained quadratic programming.

Findings

Neural network's equilibrium aligns with the optimal solution.

Proved global exponential stability of the network.

Numerical examples confirm good performance in optimization tasks.

Abstract

In this paper, a kind of neural network with time-varying delays is proposed to solve the problems of quadratic programming. The delay term of the neural network changes with time t. The number of neurons in the neural network is n + h, so the structure is more concise. The equilibrium point of the neural network is consistent with the optimal solution of the original optimization problem. The existence and uniqueness of the equilibrium point of the neural network are proved. Application inequality technique proved global exponential stability of the network. Some numerical examples are given to show that the proposed neural network model has good performance for solving optimization problems.