  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.
