Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation

Alzubi, Maen; Kovács, Szilveszter

doi:10.18517/ijaseit.9.5.8356

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…For Convex and Normal Fuzzy Sets (CNF), another method known as Incircle-FRI has been devised [18]. For all rules and observations, Incircle-FRI involves the production of CNF and PieceWise Linearity (PWL) outcomes [12], [16].…”

Section: Preliminaries and Background Related To Fuzzy Rule Interpola...mentioning

confidence: 99%

“…To ensure strict adherence to this condition, any further interpolation beyond calculation using odd points alone should be avoided (for more details see [28], [29]). To obtain a comprehensive understanding of this requirement, it is recommended that all notations of the piecewise linear condition be reviewed, along with examples, as demonstrated in [16].…”

Section: B Conservation On the Linearity Slopes (Pwl)mentioning

confidence: 99%

“…The authors in [16] presented benchmark scenarios that were constructed to assess the KH FRI's compliance with PWL requirements, serving as a reference for evaluation and comparison with other FRI methods. These scenarios were created based on various equations and notations related to the PWL property, highlighting the problematic aspects of the KH FRI method, and constructing benchmark examples for testing other FRI methods against situations that do not meet the linearity condition for KH FRI.…”

Section: B Conservation On the Linearity Slopes (Pwl)mentioning

confidence: 99%

“…A1= [4], A2= [8], A3= [16], B1= B2= [26], B3= [42], A * 1= [18] , A * 2= [26] , A * 3= [34], and Fuzzy Rules are: (A11 and A21 and A31→ B1), (A12 and A22 and A32→ B2), (A13 and A32 and A33→ B3)…”

Section: The First Approach (Iv-b1) Of the Fuzziness Condition Condit...mentioning

confidence: 99%

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Comparative Analysis of Fuzzy Rule Interpolation Techniques Across Various Scenarios Using a Set of Benchmarks

Alzubi,

Almseidin,

Alkasassbeh

et al. 2024

IEEE Access

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This paper presents a set of benchmarks to evaluate the performance of Fuzzy Rule Interpolation (FRI) methods under various challenging conditions. FRI methods are widely used for handling sparse fuzzy rule bases and reducing decision complexity. Despite lacking overlap with the antecedents of any rule in the rule bases, FRI can still produce a conclusion. To unify the requirements of FRI methods, several conditions have been proposed. Among these, the convex and normal fuzzy set condition and the Piece-wise linearity condition are the most common. In this paper, we introduce new benchmark scenarios for testing FRI methods. These benchmarks aim to serve as a reference for evaluating and comparing the accuracy and effectiveness of FRI methods. By using these benchmarks, researchers can compare different FRI methods and identify areas for improvement in the field of fuzzy inference.

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Section: Preliminaries and Background Related To Fuzzy Rule Interpola...mentioning

confidence: 99%

Section: B Conservation On the Linearity Slopes (Pwl)mentioning

confidence: 99%

Section: B Conservation On the Linearity Slopes (Pwl)mentioning

confidence: 99%

Section: The First Approach (Iv-b1) Of the Fuzziness Condition Condit...mentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis of Fuzzy Rule Interpolation Techniques Across Various Scenarios Using a Set of Benchmarks

Alzubi,

Almseidin,

Alkasassbeh

et al. 2024

IEEE Access

Self Cite

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“…The number of rules is not restricted. There are some restrictions to use FRI toolbox, only convex and normal fuzzy sets are allowed (see [20]). In case of GM the allowed set types are: singleton, triangle, trapezoid.…”

Section: A General Descriptionmentioning

confidence: 99%

Fuzzy Rule Interpolation Toolbox for the GNU Open-Source OCTAVE

Alzubi

Almseidin

Lone

et al. 2019

2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA)

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In most fuzzy control applications (applying classical fuzzy reasoning), the reasoning method requires a complete fuzzy rule-base, i.e all the possible observations must be covered by the antecedents of the fuzzy rules, which is not always available. Fuzzy control systems based on the Fuzzy Rule Interpolation (FRI) concept play a major role in different platforms, in case if only a sparse fuzzy rule-base is available. This cases the fuzzy model contains only the most relevant rules, without covering all the antecedent universes. The first FRI toolbox being able to handle different FRI methods was developed by Johanyak et. al. in 2006 for the MATLAB environment. The goal of this paper is to introduce some details of the adaptation of the FRI toolbox to support the GNU/OCTAVE programming language. The OCTAVE Fuzzy Rule Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming language, providing a large functionally compatible subset of the MATLAB FRI toolbox as well as many extensions. The OCTFRI Toolbox includes functions that enable the user to evaluate Fuzzy Inference Systems (FISs) from the command line and from OCTAVE scripts, read/write FISs and OBS to/from files, and produce a graphical visualisation of both the membership functions and the FIS outputs. Future work will focus on implementing advanced fuzzy inference techniques and GUI tools.

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A Threefold Approach for Enhancing Fuzzy Interpolative Reasoning: Case Study on Phishing Attack Detection Using Sparse Rule Bases

Almseidin,

Alzubi,

Al-Sawwa

et al. 2024

Computers

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Fuzzy systems are powerful modeling systems for uncertainty applications. In contrast to traditional crisp systems, fuzzy systems offer the opportunity to extend the binary decision to continuous space, which could offer benefits for various application areas such as intrusion detection systems (IDSs), because of their ability to measure the degree of attacks instead of making a binary decision. Furthermore, fuzzy systems offer a suitable environment that is able to deal with uncertainty. However, fuzzy systems face a critical challenge represented by the sparse fuzzy rules. Typical fuzzy systems demand complete fuzzy rules in order to offer the required results. Additionally, generating complete fuzzy rules can be difficult due to many factors, such as a lack of knowledge base or limited data availability, such as in IDS applications. Fuzzy rule interpolation (FRI) was introduced to overcome this limitation by generating the required interpolation results in cases with sparse fuzzy rules. This work introduces a threefold approach designed to address the cases of missing fuzzy rules, which uses a few fuzzy rules to handle the limitations of missing fuzzy rules. This is achieved by finding the interpolation condition of neighboring fuzzy rules. This procedure was accomplished based on the concept of factors (which determine the degree to which each neighboring fuzzy rule contributes to the interpolated results, in cases of missing fuzzy rules). The evaluation procedure for the threefold approach was conducted using the following two steps: firstly, using the FRI benchmark numerical metrics, the results demonstrated the ability of the threefold approach to generate the required results for the various benchmark scenarios. Secondly, using a real-life dataset (phishing attacks dataset), the results demonstrated the effectiveness of the suggested approach to handle cases of missing fuzzy rules in the area of phishing attacks. Consequently, the suggested threefold approach offers an opportunity to reduce the number of fuzzy rules effectively and generate the required results using only a few fuzzy rules.

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Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation

Cited by 3 publications

References 18 publications

Comparative Analysis of Fuzzy Rule Interpolation Techniques Across Various Scenarios Using a Set of Benchmarks

Comparative Analysis of Fuzzy Rule Interpolation Techniques Across Various Scenarios Using a Set of Benchmarks

Fuzzy Rule Interpolation Toolbox for the GNU Open-Source OCTAVE

A Threefold Approach for Enhancing Fuzzy Interpolative Reasoning: Case Study on Phishing Attack Detection Using Sparse Rule Bases

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