Outputs (81)

An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems (2021)
Presentation / Conference Contribution
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021, July). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. Presented at 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg

Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition app... Read More about An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems.

Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox (2021)
Presentation / Conference Contribution
Razak, T. R., Chen, C., Garibaldi, J. M., & Wagner, C. (2021, July). Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox. Presented at 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, Luxembourg

The use of Hierarchical Fuzzy Systems (HFS) has been well acknowledged as a good approach in reducing the complexity and improving the interpretability of fuzzy logic systems (FLS). Over the past years, many fuzzy logic toolkits have been made availa... Read More about Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox.

Interval-Valued Regression - Sensitivity to Data Set Features (2021)
Presentation / Conference Contribution
Kabir, S., & Wagner, C. (2021, July). Interval-Valued Regression - Sensitivity to Data Set Features. Presented at 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, Luxembourg

Regression represents one of the most basic building blocks of data analysis and AI. Despite growing interest in interval-valued data across various fields, approaches to establish regression models for interval-valued data which address and handle t... Read More about Interval-Valued Regression - Sensitivity to Data Set Features.

A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems (2020)
Journal Article
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2021). A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(11), 3323-3333. https://doi.org/10.1109/TFUZZ.2020.3018379

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainabil... Read More about A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems.

Insights from interval-valued ratings of consumer products - a DECSYS appraisal (2020)
Presentation / Conference Contribution
Ellerby, Z., Miles, O., McCulloch, J., & Wagner, C. (2020, July). Insights from interval-valued ratings of consumer products - a DECSYS appraisal. Presented at 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, United Kingdom

The capture and analysis of interval-valued data has seen increased interest over recent years. This offers a direct means to capture and reason about uncertainty in data, whether obtained from sensors or from people. Open-source software (DECSYS [1]... Read More about Insights from interval-valued ratings of consumer products - a DECSYS appraisal.

Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach (2020)
Journal Article
Soria, D., Razak, T. R., Garibaldi, J. M., Pourabdollah, A., & Wagner, C. (2021). Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems, 29(5), 1160-1172. https://doi.org/10.1109/tfuzz.2020.2969901

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as: "How can we measure their interpretability?", "How can we make an informed ass... Read More about Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach.

Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security (2019)
Presentation / Conference Contribution
Ellerby, Z., McCulloch, J., Wilson, M., & Wagner, C. (2019, September). Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security. Presented at CRITIS 2019 : 14th International Conference on Critical Information Infrastructures Security, Linköping, Sweden

Subjective judgements from experts provide essential information when assessing and modelling threats in respect to cyber-physical systems. For example, the vulnerability of individual system components can be described using multiple factors, such a... Read More about Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security.

On the Choice of Similarity Measures for Type-2 Fuzzy Sets (2019)
Journal Article
McCulloch, J., & Wagner, C. (2020). On the Choice of Similarity Measures for Type-2 Fuzzy Sets. Information Sciences, 510, 135-154. https://doi.org/10.1016/j.ins.2019.09.027

Similarity measures are among the most common methods of comparing type-2 fuzzy sets and have been used in numerous applications. However, deciding how to measure similarity and choosing which existing measure to use can be difficult. Whilst some mea... Read More about On the Choice of Similarity Measures for Type-2 Fuzzy Sets.

ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems (2019)
Journal Article
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2020). ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems, 28(10), 2302-2312. https://doi.org/10.1109/tfuzz.2019.2933787

Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to capture and handle input noise within the design of input fuzzy sets. In this paper, we propose an online learning method which utilises a sequence of observations to continuously updat... Read More about ADONiS - Adaptive Online Non-Singleton Fuzzy Logic Systems.

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels (2019)
Presentation / Conference Contribution
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019, June). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input... Read More about Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels.