The dryness of turbine's exhaust steam can hardly be obtained via direct measurements in online performance calculation of thermal system

for the steam is usually in wet state.The artificial's neural networks was established to calculate the exhaust enthalpy of steam turbine.By analyzing the main factors affecting the exhaust enthalpy and transforming the original data into non-dimensional parameters

the calculation speed and accuracy were compared by using different training functions in Back-Propagation(BP)neural network.Meanwhile

the accuracy of the BP Neural Network and the Radial Basis Function(RBF)network was compared as well.Consequently

when calculating the exhaust enthalpy

the BP neural network depends strongly on training functions

some of which will result in strong random and low calculation speed.After the research

three of the training functions:'traingdx'

'trainscg'and'trainoss'

have the advantages of both fast calculation speed and high accuracy.The RBF network has the advantages of faster calculation speed and weaker dependence on training functions but the disadvantage of lower accuracy

compared with the BP neural network.The error will be diminished when enough training samples are provided.