数字工厂研究室知识库

本发明涉及一种基于自学习机制的快速匹配模糊推理方法，包括以下步骤：采用高斯隶属度函数法构建参数模糊化信息；建立模糊规则库；将外部参数数据进行模糊化得到事实项，采用rete算法将事实项与模糊规则库中的规则进行匹配，得到模糊推理结果；将模糊推理结果进行去模糊化得出最终推理结果；根据最终推理结果和实际反馈结果构建样本集，基于样本集进行规则强度自学习修正。本发明采用rete算法提高了模糊推理的效率，使模糊推理方法能适用于对实时性要求较高的工程领域。

The invention relates to a self-learning mechanism-base fast matching fuzzy reasoning method. The method includes the following steps that: a Gaussian membership degree function method is adopted to construct parameter fuzzification information; a fuzzy rule base is established; external parameters are fuzzificated, so that a fact item can be obtained; the fact item is matched with rules in the fuzzy rule base by adopting a rete algorithm, so that a fuzzy reasoning result can be obtained; the fuzzy reasoning result is subjected to defuzzification, so that a final reasoning result can be obtained; and a sample set is constructed according to the final reasoning result and an actual feedback result, and rule strength self-learning correction is carried out based on the sample set. According to the self-learning mechanism-base fast matching fuzzy reasoning method of the invention, the rete algorithm is adopted, so that the efficiency of fuzzy reasoning can be improved, and the fuzzy reasoning method can be applied to the engineering field with high real-time requirements.