Self-learning mechanism-base fast matching fuzzy reasoning method

Abstract

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.

Description

technical field [0001] The invention relates to a fast matching fuzzy reasoning method, in particular to a fast matching fuzzy reasoning method based on a self-learning mechanism. This method belongs to the field of artificial intelligence. Background technique [0002] With the development of high technologies such as electronic information, aerospace, resources and environment and the expansion of people's exploration of natural fields, the automation level of the system is expanding day by day, and the complexity is increasing rapidly. It is particularly important to ensure the reliability and efficiency of complex system operation, especially for aerospace, navigation, nuclear industry, etc., which put forward higher requirements for the efficiency, stability and reliability of the system, and intelligent decision-making technology provides this The requirements have opened up a new approach. Intelligent decision-making mainly includes production decision adjustment, fa...

AI Technical Summary

Problems solved by technology

[0004] Most of the current fuzzy reasoning technology adopts the forward reasoning method. When the number of knowledge rules is large, explosive combination of knowledge is easy to occur, resulting in low efficiency of pattern matching. On the other hand, the construction of fuzzy knowledge base in fuzzy reasoning technology mainly relies on Due to the experience of experts, in the process of using the fuzzy reasoning system, it mainly relies on experts to maintain the knowledge base, and lacks a self-learning correction mechanism based on reasoning result samples

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