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The spread of COVID-19 has triggered one of the largest pandemics in modern human history. Humanity is still in the incomplete information period for this infectious disease, and how to effectively deal with such a major public crisis is a crucial problem. Although there are divergences in human natural semantics, the incomplete information increases it. Therefore, this study integrates the neutrosophic set and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to explore the key factors which would prevent expansion of the epidemic in the face of incomplete knowledge. The neutrosophic set technique is an effective tool for the representation of the ambiguity of natural human semantic expression, for the analysis of incomplete, uncertain, and inconsistent information. DEMATEL is used to explore the causes and effects between factors and to generate an influential network relationship map. The results of analysis can help the government and relevant organizations to understand the cause and effect relationship between the factors and set appropriate prevention strategies. The results of this study show that the incorporation of neutrosophic set theory leads to a more meaningful evaluation under incomplete information. “Detect” is a key factor affecting the entire system. The results of this study contribute to the advancement and development of scientifically based decision-making by helping governments and relevant organizations to understand the causal relationships between factors, to set appropriate prevention strategies.

AbstractA self-organizing neuro-fuzzy compensator which cooperates with a sliding mode controller is proposed to tackle physical learning control problems in this paper. The sliding mode controller is used to drive the state variables to a specific switching hyperplane or a desired trajectory. A fuzzy cerebellar model articulation controller (FCMAC) with self-organizing capability functions as a forward compensator to reduce the chattering effect or cancel the influence of system uncertainties. The method is actually based on the concept of the conventional CMAC and reinforcement learning. Simulations on balancing a cart-pole system are given to illustrate the performance and applicability of the proposed control scheme.