Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote

• This paper developed a new variable selection method for soft sensor applications.
• The method combined artificial neural network with nonnegative garrote, and could describe highly nonlinear industrial process accurately.
• The proposed method was compared with three state-of-art methods.
• The proposed method was successfully applied on a real industrial application for air separation process.

This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods.