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A novel optimization algorithm for multilayer perceptron- (MLP-) based soft sensors is proposed in this paper. The proposed approach integrates input variable selection and hidden layer optimization on MLP into a constrained optimization problem. The nonnegative garrote (NNG) is implemented to perform the shrinkage of input variables and optimization of hidden layer simultaneously. The optimal garrote parameter of NNG is determined by combining cross-validation with Hannan-Quinn information criterion. The performance of the algorithm is demonstrated by an artificial dataset and the practical application of the desulfurization process in a thermal power plant. Comparative results demonstrated that the developed algorithm could build simpler and more accurate models than other state-of-the-art soft sensor algorithms.

A Novel Input Variable Selection and Structure Optimization Algorithm for Multilayer Perceptron-Based Soft Sensors