۱٫ Introduction

۲٫ Intuitionistic Fuzzy Reasoning

۳٫ Dynamic Bayesian network

۴٫ Intuitionistic Fuzzy Dynamic Bayesian Network

۵٫ Case study

۶٫ Conclusions

Acknowledgement

References

The period of terminating situation in air combat is time-varying and full of information with uncertainty. In order to process the fuzzy information in time series and assess the situation effectively, Intuitionistic Fuzzy Dynamic Bayesian Network (IFDBN) is built. In this model, intuitionistic fuzzy reasoning engine is embedded into dynamic Bayesian Network as a virtual node. Besides, a new method to convert the intuitionistic fuzzy reasoning output into probability which can be input into dynamic Bayesian Network as virtual evidence is proposed. On these bases, the detailed intuitionistic fuzzy reasoning algorithms are formulated and the dynamic Bayesian Network information transferring mode is analyzed. Finally, a case study for terminating situation assessment in air combat is given to prove the feasibility of the proposed model.

Introduction

At the core of nonlinear reasoning techniques is Bayesian Network (BN), which is widely used because of its information processing system and strong reasoning ability. However, terminating situation has special features and differences from situation in conventional mode. On one hand, the combat in the period of terminating situation is full of fuzzy information and data which always appear in huge volume2 . On the other hand, terminating situation is in rapid time series, which requires the method to consider the factor of time. Therefore, using the traditional knowledge representation and reasoning ability of BN cannot meet the demand of terminating situation assessment. In order to describe the situation in time-varying state, the improvement of dynamic Bayesian Network has aroused wide concerns, and many academic achievements have been done. Chai et al. Reference 3 analyze the causal relationship between different elements in Bayesian Network under continuous time slices and incorporates time series factor into BN, which maintains the robust of model.