基于极大模糊熵原理的模糊产生式规则中的权重获取方法研究

Research on Learning Weights of Fuzzy Production Rules Based on Maximum Fuzzy Entropy

模糊产生式规则(IFTHEN规则)是不确定性知识表示的一种最基本的最常用的形式,在模糊规则中引入权重,能增强模糊规则对待分类示例的泛化能力.模糊产生式规则的一项重要研究工作就是权重如何获取.目前常用的权重获取准则是依据于训练精度的提高,这种方法的明显不足就是会引起过度拟合.因此,提出了一种新的基于极大模糊熵原理的权重获取准则.在保证不降低训练精度的前提下,调整权重来极大化训练集的模糊熵,能有效提高测试精度.新的权重获取策略有效解决了过度拟合问题,同时提高了测试精度.

Fuzzy production rules （FPRs） is a fundamental and important way of imprecise knowledge representation. For enhancing generalization capability of FPRs for the given examples, the concept of weight is introduced into FPRs. So it is necessary to explore specific criterion for determining these weight values. Generally speaking, the usual criterion of the weight values adjustment, which is based only on improving training accuracy, often results in an over-fitting. This paper aims to accomplish this task by using a new method based on the well-known maximum fuzzy entropy principle. In the case that the training accuracy does not decrease, the testing accuracy will increase with the value of fuzzy entropy of training set. At the same time, adjusting the weight values can change the fuzzy entropy of training set. Therefore, this new criterion can avoid the drawback of over-fitting and can improve the testing accuracy.