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
enhance 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 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 in a case study, 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.