Design of an Adaptive Nonnegative Garrote Algorithm for Multi-Layer Perceptron- Based Soft Sensor

Abstract

In the study, we propose an adaptive variable selection algorithm for multi-layer perceptron (MLP)-based soft sensors. The proposed algorithm employs nonnegative garrote (NNG) to shrink the input weights of the trained MLP. To improve the shrinkage efficiency of the NNG, adaptive operators are designed using the mean impact value estimate. Moreover, the adaptive operators are data dependent, and are introduced into the constraints of NNG to make the shrinkage more efficient and effective. Cross-validation and Bayesian information criterion are used to determine the optimal garrote parameter for the NNG. The performance of the algorithm is validated using artificial datasets and a practical industrial application in coke dry quenching (CDQ) systems. The simulation results demonstrate that the adaptive mechanism improves the efficiency and precision of NNG, and has superior performance to other state-of-the-art algorithms. The result of variable selection is consistent with the experience of the field operator, and can provide technical supports for the optimisation and control of the CDQ process.