Affiliations: [a]
National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC | [b] Department of Computer Science, National Pingtung University, Pingtung, Taiwan, ROC | [c] Department of Mechanical Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan, ROC | [d] Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC | [e] Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan, ROC
Correspondence:
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Corresponding authors. Wei-Tai Huang, Department of Mechanical Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan, ROC. E-Mail: [email protected] and Wen-Hsien Ho, Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC. E-Mail: [email protected].
Abstract: The uncertainty issue in real-work optimization affects the level of optimization significantly. Because most future uncertainties cannot be foreseen in advance, the optimization must take the uncertainties as a risk in an intelligent way in the process of computation algorithm. Based on our risk-sensitive filtering algorithm, this study adopts a model-predictive control to construct a risk-averse, predictable model that can be used to regulate the level of a real-world system. Our model is intelligent in that the predictive model needs not to identify the system parameters in advance, and our algorithm will learn the parameters through data. When the real-world system is under the disturbance of unexpected events, our model can still maintain suitable performance. Our results show that the intelligent model designed in this study can learn the system parameters in a real-world system and minimize unexpected real-world disturbances. Through the learning process, our model is robust, and the optimal performance can still be retained even the system parameters deviate from expected, e.g., material shortage in a supply chain due to earthquake. When parameter error risks occur, the control rules can still drive the overall system with a minimal performance drop.
