基于函数S-粗集的Bellman原理优化算法在知识测度的应用

The Application of Optimization Algorithm Basedon Function S-Rough Sets Bellman Principle

应用函数双向S-粗集理论实现参考模式和测试模式的动态模式匹配,用Bellman原理的动态规划算法实现全局约束定义下的Levenstein距离的计算,以此确定出参考模式和测试模式的距离测度,有明显降低计算复杂度的效果,并以无纸考试系统非标准化试题的智能评分为例进行说明。

This paper used the theory of Function S-Rough Sets to realize the matching of dynamic mode between reference mode and test mode, and then calculate the Levenstein distance with dynamic programming algorithm of Bellman principle. Taking the intelligence estimate of nonstandardization test questions of no paper examination system as an example, It is found that the Knowledge distance measure which can reduce the effect of computation complexity greatly.