Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking
This work addresses the need for faster and more reliable solving models in time-varying quadratic minimization, with applications like target tracking, but appears incremental as it builds on existing neural dynamics methods.
The paper tackles the time-varying quadratic minimization problem by proposing an adaptive zeroing-type neural dynamics model that adjusts step size online and includes an integration term for robustness, resulting in faster convergence and more reliable performance than existing approaches.
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.