基于改进模糊置信规则的意图识别方法

An Intention Recognition Method Based on Fuzzy Belief-Rule-Base

针对传统意图识别方法只能处理某种类型不确定性信息的不足,该文结合模糊集和DS证据理论优势提出一种模糊置信规则库(BRB)信息处理方法。首先在置信规则前提部分改进了前提属性的连接关系,根据数据集统计分布特点设计了模糊集分割,选取Cauchy型分布作为隶属度函数,较好地避免置信规则无法被有效激活进而导致系统无有效输出问题;其次融合处理辨识框架内不同类别的置信分布,建立规则权重和特征权重优化模型,构建了特征空间与类别空间之间的输入输出关系;在此基础上,计算未知意图数据在相应规则模糊域的匹配度和激活度,采用置信度最大原则进行识别决策。通过实验验证、参数敏感性及结果分析、时间复杂度分析,表明该文方法可以获得比其他识别方法更高的正确率,尤其是在小样本条件下更能体现出该方法的有效性和可靠性。

Considering the deficiency that traditional intention recognition methods can only deal with certain types of uncertain information,a new information processing method of fuzzy Belief-Rule-Base(BRB)is proposed,which combines the advantages of fuzzy sets and Dempster-Shafer(DS)theory.Firstly,the connection relation of premise attributes is improved in the premise part of confidence rules,and fuzzy set segmentation is designed according to the statistical distribution characteristics of data sets.Cauchy distribution is selected as membership function to avoid the problem that confidence rules could not be activated effectively,which would lead to no effective output of the system.Secondly,the confidence distribution of different categories in the identification framework is fused,and the optimization model of rule weight and feature weight is established,and the input-output relationship between feature space and category space is constructed.On this basis,the matching degree and activation degree of the unknown intention data are calculated,and the recognition decision is made using the maximum confidence principle.Through experimental verification,sensitive parameter and interpretation of result,time complexity analysis,compared with other methods,the fuzzy belief-rule-base shows high accuracy rate,and effectiveness and reliability under the condition of small samples.