Wu, J.; Lu, Y.; Li, D.; Zhou, W.; Huang, J. Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference. Appl. Sci.2023, 13, 10575.
Wu, J.; Lu, Y.; Li, D.; Zhou, W.; Huang, J. Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference. Appl. Sci. 2023, 13, 10575.
Wu, J.; Lu, Y.; Li, D.; Zhou, W.; Huang, J. Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference. Appl. Sci.2023, 13, 10575.
Wu, J.; Lu, Y.; Li, D.; Zhou, W.; Huang, J. Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference. Appl. Sci. 2023, 13, 10575.
Abstract
In complex systems constrained by multiple factors, it is of great significance to accurately identify the key influencing factors for mastering the evolution and development law of the system and obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; the method based on decision trial and Evaluation laboratory (DEMATEL) involves a wide range of subjects and is greatly influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. The model uses the FCI algorithm with prior knowledge to learn the global causal network among multiple factors of the complex system. The causal effect among variables in the causal network is calculated by using heuristic causal inference method. Specifically, the causal path contribution degree of cause variable to target variable is calculated to replace the causal effect of each cause variable to target variable. The key influencing factors in the system are screened out according to the contribution degree of causal pathways. Based on the dataset generated in the production process of a semiconductor manufacturing system, we carried out simulation experiments, identified several factors that have a key impact on product quality, and proved the feasibility and effectiveness of the proposed model.
Copyright:
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