Back propagation(BP) neural network model was used to study the dependency between the structural parameters and the stability of reverse unloading diaphragm pressure reducing regulator(PRR).The stability of PRR obtained by adjusting structural parameters

in particular

multi-structure parameters

proves that the stability of PRR is very sensitive to the diameter of damping orifice and the stiffness of diaphragm

and also relatively sensitive to the damping coefficient of springy elements material and the effective length of the low-pressure chamber.Thus

various measures for improving the dynamic stability were proposed

including increasing the diameter of damping orifice

the stiffness of diaphragm

the damping coefficient of springy elements material within a certain range(6.5 times of standard value)

the effective length of the low-pressure chamber and reducing the mass of valve spool.The BP neural network model tested by the error analysis of the numerical experiments does not show any phenomenon of over-fitting and local optimum.The predictions of the model are reliable

which can be used to support the decision for the design of the PRR and system analysis.In addition

the model is applicable for different data sets

and can be used to study the dependency between the structural parameters and the performance of other components.