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

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.

Automated summary

This paper proposes a new information processing method called fuzzy Belief-Rule-Base (BRB), which addresses the deficiency of traditional intention recognition methods in handling uncertain information. The method combines the advantages of fuzzy sets and Dempster-Shafer (DS) theory. Experimental verification shows that the fuzzy belief-rule-base achieves high accuracy, effectiveness, and reliability under the condition of small samples.