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

• Published in: IEEE sensors journal Vol. 21; no. 19; pp. 21808 - 21816
• Main Authors: Sun, Kai, Sui, Lin, Wang, Hongxun, Yu, Xiaodong, Jang, Shi-Shang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithms; Algorithms; Artificial neural networks; Feature selection; Industrial applications; Input variables; mean impact value; Multi-layer perceptron; Multilayer perceptrons; nonnegative garrote; Operators; Optimization; Prediction algorithms; Sensors; Shrinkage; soft sensor; Sun; variable selection
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3102586

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.