In order to explore the application of artificial neural network in rehabilitation evaluation, a kind of ANN stable and reliable artificial intelligence algorithm is proposed. By learning the existing clinical gait data, this method extracted the gait characteristic parameters of patients with different ages, disease types and course of disease, and repeated data iteration and finally simulated the corresponding gait parameters of patients. Experiments showed that the trained ANN had the same score as the human for most of the data (82.2%, Cohen’s kappa = 0.743). There was a strong correlation between ANN and improved Ashworth scores as assessed by human raters (r = 0.825,
P
<
0.01
). As a stable and reliable artificial intelligence algorithm, ANN can provide new ideas and methods for clinical rehabilitation evaluation.
Aiming at the direct methanol fuel cell system is too complicated, difficult
to model, and the thermal management system needs to be optimized. The
article attempts to bypass the internal complexity of direct methanol fuel
cell, based on experimental data, use neural networks to approximate
arbitrarily complex non-linear functions ability to apply neural network
identification methods to direct methanol fuel cell, a highly non-linear
thermal management system optimization modelling. The paper uses 1000 sets
of battery voltage and current density experimental data as training samples
and uses an improved back propagation neural network to establish a battery
voltage-current density dynamic response model at different temperatures.
The simulation results show that this method is feasible, and the
established model has high accuracy. It makes it possible to design the
real-time controller of the direct methanol fuel cell and optimize the
thermal energy manage?ment system?s efficiency.
The thesis simulates the engine?s installation and uses conditions in the
whole vehicle, such as the water tank, fan, the engine?s arrangement in the
engine room, accessories and pipe-line connections, etc. to build a test
bench for the engine thermal management system. According to the thermal
management simulation analysis software KULI modelling, the article designs
the bench test conditions according to the parameter input requirements of
the thermal management simulation analysis software. The accuracy of the
model is verified by comparing simulation and test data, and the NEDC
driving cycle is used to simulate the performance of the vehicle cooling
system to guide the selection and matching of thermal management system
components.
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