Аннотация:
Integral nonlinear models are used to simulate the human eye movement system (EMS) while accounting
for its nonlinear dynamics and inertial properties. Multidimensional transient characteristics (MTCs) of
the EMS were identified based on experimental input-output data obtained from eye-tracking responses
to visual test stimuli. These transient characteristics include first-order and diagonal cross-sections up to
the second and third orders of MTCs. The study aimed to evaluate the accuracy of EMS simulation models
by analyzing the calculation errors of transient characteristics using nonlinear dynamic identification
methods based on Volterra integro-power series (IPS) and integro-power polynomial (IPP). Computational
methods, including the least squares method (LSM), approximation, and compensation, were used to
derive the models. Models developed using the LSM and approximation methods produced consistent
transient characteristics when the same test signals were applied, highlighting the convergence of the
Volterra series within the identified region. The findings showed that increasing the number of test
signals enhanced the accuracy of the EMS models. Quadratic models were identified as the most reliable,
providing a balance between precision and computational efficiency. Cubic models closely matched EMS
responses but exhibited instability in their transient characteristics, making them less practical for EMS
application. The compensation method, while computationally less demanding, proved unsuitable for
tasks requiring high accuracy due to significant errors in the resulting models. Quadratic IPP models
developed with LSM based on three response datasets are recommended for future studies, as they
provide a stable and precise framework for modeling EMS dynamics and exploring psychophysiological
state assessment.
