A dynamical recurrent neural network

i.e

Elman neural network

has been used to predict vacuum in the condenser

through calculation in real example

it shows that the said method can more accurately predict vacuum in the condenser

and boasting features of high training speed

simple structure

and high accuracy

being an effective and feasible prediction method.At the same time

owing to occurrence of partial minimum values in back propagation(BP) algorithm

a method for optimizing the connecting weight value coefficient of Elman neural network has been put forward by using the global optimization capability of particle swarm optimization(PSO) algorithm.Results of emulation show that a model for more accurately predicting vacuum in the condenser can be established by using the PSO algorithm and Elman neural network.