Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking

TL;DR

This paper introduces an adaptive zeroing-type neural dynamics model that improves convergence speed and robustness for solving time-varying quadratic minimization problems, with successful application to target tracking.

Contribution

It proposes a novel adaptive coefficient framework for neural dynamics, enhancing solution speed and robustness in TVQM problems and demonstrating practical target tracking applications.

Findings

Faster convergence compared to existing methods

Enhanced robustness in perturbed environments

Effective application in target tracking scenarios

Abstract

The time-varying quadratic miniaturization (TVQM) problem, as a hotspot currently, urgently demands a more reliable and faster--solving model. To this end, a novel adaptive coefficient constructs framework is presented and realized to improve the performance of the solution model, leading to the adaptive zeroing-type neural dynamics (AZTND) model. Then the AZTND model is applied to solve the TVQM problem. The adaptive coefficients can adjust the step size of the model online so that the solution model converges faster. At the same time, the integration term develops to enhance the robustness of the model in a perturbed environment. Experiments demonstrate that the proposed model shows faster convergence and more reliable robustness than existing approaches. Finally, the AZTND model is applied in a target tracking scheme, proving the practicality of our proposed model.