A method is described for improving the prediction accuracy and generalization performance of 
artificial neural network models in presence of input-output example data containing instrumental 
noise and / or measurement errors, the presence of 
noise and / or errors in the input-output example data used for training the network models create difficulties in learning accurately the nonlinear relationships existing between the inputs and the outputs, to effectively learn the noisy relationships, the methodology envisages creation of a large-sized 
noise-superimposed sample input-output dataset using computer simulations, here, a specific amount of 
Gaussian noise is added to each input / output variable in the example set and the enlarged sample 
data set created thereby is used as the 
training set for constructing the 
artificial neural network model, the amount of noise to be added is specific to an input / output variable and its optimal value is determined using a stochastic search and optimization technique, namely, genetic algorithms, the network trained on the noise-superimposed enlarged 
training set shows significant improvements in its prediction accuracy and generalization performance, the invented methodology is illustrated by its successful application to the example data comprising instrumental errors and / or measurement noise from an industrial 
polymerization reactor and a 
continuous stirred tank reactor (CSTR).