MODELING INTELLIGENT EXPERT SYSTEMS BASED ON FUZZY LOGIC AND FUZZY PRODUCTION RULES

Abstract

This article provides a detailed analysis of fuzzy logic and fuzzy production rules, which play a significant role in artificial intelligence and expert systems. The author justifies the relevance of modeling fuzzy inference for decision-making processes based on incomplete, uncertain, and subjective information in real-world scenarios. Using the example of an automatic water level control system, membership functions were developed and an inference algorithm was designed to determine the valve opening angle. Through this, expert knowledge was formalized into an intelligent control model that can be implemented on a computer. The article systematically covers the stages of fuzzification, rule-based inference, aggregation, and defuzzification, with practical aspects supported by graphical analysis. A methodology for generating membership functions through expert interviews, observation, and experimentation is proposed. The results demonstrate that fuzzy models are highly effective in formalizing human reasoning and developing adaptive control systems. The approach is recommended for application in fields such as medicine, education, and economics, where decision-making often involves uncertainty and subjectivity.

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References

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