A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables

A fuzzy cognitive map (FCM) is an analysis tool that uses a graph structure to show the causal relationships of influence factors in a decision-making system. During the decision-making process, it is reasonable for experts to use linguistic variables to express their subjective opinions. However, few studies have discussed methods for aggregating the linguistic opinions of experts to reach a group consensus in FCM. In addition, the interaction weights among the factors in FCM will usually change over time in a real environment. Therefore, we applied the learning algorithm to adjust the interaction weights among the factors in the steps of FCM, after which we proposed a dynamic fuzzy cognitive map model with group consensus based on the linguistic evaluations in this study. Finally, we presented a case study using the proposed model to illustrate the development possibility of the Internet of Things (IoT) industry in Taiwan. According to the analysis results, we found that the development prospects for the IoT industry in Taiwan are optimistic. The four key factors for IoT industry development were found to be the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment and cross-platform possibility.

Automated summary

The study proposed a dynamic fuzzy cognitive map model with group consensus based on linguistic evaluations for analyzing the development possibility of the Internet of Things industry in Taiwan. The research found optimistic prospects for the development of the IoT industry in Taiwan, with key factors including the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment, and cross-platform possibility.