Extracting Control Rules through Fuzzy Logic using Machine Learning Methods from Home Automation Dataset

Abstract

Fuzzy logic is a mathematical framework that allows for uncertain or imprecise reasoning, making it particularly useful in situations where traditional binary logic is insufficient. The proposed method used decision trees, which are examples of machine learning algorithms that can analyze data and produce fuzzy logic-based control rules. The extracted rules used in process control, robotics, and automation applications of the proposed technology are highlighted, focusing on merging machine learning with fuzzy logic to build flexible and powerful intelligent systems. The paper explores methodology, core ideas, and applications related to machine learning-based extraction of control rules from fuzzy logic. Numerous rules are produced as a result of this approach to help direct desired results. The necessity for interpretability in control rules generated by machine learning algorithms is one of the major issues raised in the passage. This is a reference to the significance of comprehending and outlining how certain guidelines are created and the rationale behind specific judgements. In many fields, interpretability is essential because it promotes trust and comprehension of the system's behaviour and ensures that the judgements made by the system are sound.