Abstract: As a tool for studying uncertain information,cloud model is of great significance in uncertain artificial intelligence and data mining.The backward cloud transformation algorithm is one of the important algorithms of cloud model,which can realize the transformation from quantitative data to qualitative concepts.This paper mainly studies the backward cloud transformation algorithm from the perspective of dynamic increment.Firstly,the irrationality of parameter estimation in the existing classical backward cloud transformation algorithm based on the first-order absolute central moment is analyzed theoretically.Secondly,on the basis of theoretical analysis,combined with the characteristics of cloud droplets generated by the forward cloud transformation algorithm,the normal random variable is used to dynamically generate new cloud droplets as new samples,then the randomly generated samples and the original samples are fused as the final samples to estimate the parameters,which effectively solves the estimation problems existing in the existing algorithms.Therefore,two dynamic incremental backward cloud transformation algorithms are proposed.Thirdly,through random simulation experiments,this paper compares the proposed backward cloud transform algorithm with existing algorithms from four aspects:effectiveness,stability,convergence and parameter robustness.The experimental results show that the dynamic incremental backward cloud transformation algorithm proposed in this paper has smaller estimation error,better stability and convergence,and has strong robustness to parameter changes.Finally,the proposed backward cloud transform algorithm is applied to the simulation and evaluation of Shooters' shooting level.The experimental results further show that the proposed algorithms have preferably practicability.

Key words: Cloud model, Backward cloud transformation, Cloud drop, Dynamic increment, Hyper entropy