Qualitative and quantitative knowledge of metasynthesis based on fuzzy system

The metasynthesis has been widely used to solve complex system problems, and its core is the combination of human and machine, from qualitative to quantitative iterative solving. But how to describe and integrate qualitative and quantitative knowledge effectively is still the urgent problem to be solved. The fuzzy system simulates the reasoning process of human brain. It can not only use the qualitative knowledge of experts, but also learn fuzzy rules from the data, and use the way of rule mapping to realize the system decision of uncertain problems. By introducing the fuzzy system into the process of description, comprehension and fusion of qualitative and quantitative knowledge, the interpretable metasynthesis based on fuzzy system was proposed. Knowledge was obtained from quantitative and qualitative perspectives, and then the two kinds of knowledge were integrated to form a fuzzy rule base and completed the fuzzy system modeling. This method effectively combines expert experience with data learning, enhances the interpretability of the model, and improves the robustness and scientificity of the decision-making process of complex systems. This method is expected to be a realization method for the research of integrated method in the future, so as to better solve the problem of system complexity in the real world.