A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering

Razak, Tajul Rosli; Garibaldi, Jonathan M.; Wagner, Christian

doi:10.1109/fuzz-ieee.2019.8859011

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…They have been applied to various problems and have demonstrated their ability to generate more resilient and cost-effective solutions in the face of uncertainties. The interpretability of FLSs is often highlighted as one of their strengths, as they enable the modelling and processing of linguistic variables and rules that are easily understandable [15]. This interpretability is particularly valuable in applications such as knowledge extraction and decision support [16]- [18].…”

Section: A Fuzzy Logic Systemmentioning

confidence: 99%

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Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Anuar,

Razak,

Kamis

2024

AJSE

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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.

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Section: A Fuzzy Logic Systemmentioning

confidence: 99%

“…Razak et al suggested a method for evaluating the complexity of HFSs that takes into account the complexity of its structure, which requires several subsystems, layers, and a dynamic topology [15], [53]. Also, the approach seems to be better with its combined structure and rule-based complexity.…”

Section: B Complexitymentioning

confidence: 99%

Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Anuar,

Razak,

Kamis

2024

AJSE

View full text Add to dashboard Cite

show abstract

“…Razak et al suggested a method for evaluating the complexity of HFSs that takes into account the complexity of its structure, which requires several subsystems, layers, and a dynamic topology [35], [36]. Also, the approach seems to be better has its combined structure complexity and rule-based complexity.…”

Section: Complexitymentioning

confidence: 99%

Hierarchical Fuzzy Systems: Interpretability and Complexity

Razak

Fauzi

Gining

et al. 2021

IJEEI

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Rule explosion related to an increase in the number of input variables has been recognised as a key issue in fuzzy logic systems (FLSs), and hierarchical fuzzy systems (HFSs) have been proposed as a viable solution. The typical FLS subsystem system is transformed into a low-dimensional FLS subsystem network in HFS. Furthermore, because the number of input variables in each subsystem is reduced, the rules in HFS generally contain antecedents with fewer variables than the rules in regular FLS with similar functions. As a result, HFSs can reduce rule explosion, reducing model complexity and improving model interpretability. Nonetheless, the concerns concerning the issue of "Does reducing the complexity of HFSs with various subsystems, layers, and diverse topologies actually increase their interpretability?" remain unclear. In this study, we compare two HFS topologies: parallel and serial, concentrating on interpretability and complexity. For both topologies, a full measurement of the interpretability and complexity with various configurations is presented. The goal of this comparative study is to see if there is a correlation between HFS interpretability and complexity.

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Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems

Varshney

Torra

2023

Int. J. Fuzzy Syst.

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Fuzzy rule-based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human-understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system, hierarchical fuzzy system, neuro fuzzy system, evolving fuzzy system, FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010–2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.

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A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering

Cited by 7 publications

References 32 publications

Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Advancing Fuzzy Logic: A Hierarchical Fuzzy System Approach

Hierarchical Fuzzy Systems: Interpretability and Complexity

Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems

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