Seafarer's Competency Synthetic Assessment Based on Cloud Models

In order to process the uncertainty information in assessing human element, in this paper, a synthetic assessment method is proposed to seafarers’ competency based on cloud models, where various uncertainties in the system are well kept and modeled by cloud theory. The presented approach focuses on effectively modeling the quantitative and qualitative variables of attribute factors of rules’ antecedents and seccedents, and the optimal cloud models based on the relative cloud theory are found. Then the post-condition cloud of corresponding rule is fired by foregoing cloud models through ‘logic and’ or ‘soft-and’ operator. Furthermore, fuzzy comprehensive evaluation is utilized to build rule base. Finally, the output will be constructed by means of backward cloud. That is the final assessment result which is depicted by the cloud graph in the original synthetic evaluation cloud models. Since the post-condition’ activated operators are different, experiments are conducted to analyze the different effects. Experiments are also conducted to compare the proposed paradigm with the fuzzy synthetic judgment in seafarers’ competency assessment. Simulation research shows that the proposed algorithm is more intuitive, flexible, reliable and consistent with human thinking.