Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Abstract

Fuzzy logic systems (FLS) are widely used in various engineering, medical, and scientific applications for modelling complex and uncertain systems. However, traditional FLS has limitations in handling complex and hierarchical structures due to their lack of scalability and interpretability. This paper proposes an approach to hierarchical fuzzy systems (HFS) that enhances the traditional FLS by providing a hierarchical structure with multiple levels of fuzzy rules. The main contribution of this paper is the proposal of HFS, which improves interpretability, scalability, and accuracy compared to traditional FLS, particularly for real-world applications. However, the question arises, "How can the FLS be converted into the HFS?" In this paper, the approach to HFS architecture will comprise two levels of FLS, where the first level determines the overall behaviour of the system, and the second level refines the output by considering the local behaviour. The proposed approach has been validated through experimental results on a case studies, such as the Iris flower classification. The results demonstrate that HFS provides more efficient and reliable solutions and can be applied to various complex and hierarchical systems in different domains, such as manufacturing, robotics, and decision-making.