The contribution deals with the use of artifi cial neural networks for prediction of steel atmospheric corrosion. Atmospheric
corrosion of metal materials exposed under atmospheric conditions depends on various factors such as
local temperature, relative humidity, amount of precipitation, pH of rainfall, concentration of main pollutants and
exposition time. As these factors are very complex, exact relation for mathematical description of atmospheric corrosion
of various metals are not known so far. Classical analytical and mathematical functions are of limited use to
describe this type of strongly non-linear system depending on various meteorological-chemical factors and interaction
between them and on material parameters. Nowadays there is certain chance to predict a corrosion loss of
materials by artifi cial neural networks. Neural networks are used primarily in real systems, which are characterized
by high nonlinearity, considerable complexity and great diffi culty of their formal mathematical description.Web of Science52338137