Outputs (15)

FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation (2021)
Conference Proceeding
Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2021). FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494456

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have c... Read More about FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation.

Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox (2021)
Conference Proceeding
Razak, T. R., Chen, C., Garibaldi, J. M., & Wagner, C. (2021). Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ45933.2021.9494485

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.

An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems (2021)
Conference Proceeding
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz45933.2021.9494472

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.

Comparing Intervals Using Type Reduction (2020)
Conference Proceeding
Runkler, T. A., Chen, C., Coupland, S., & John, R. (2020). Comparing Intervals Using Type Reduction. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/fuzz48607.2020.9177675

Many decision making processes are based on choosing options with maximum utility. Often utility assessments are associated with uncertainty, which may be mathematically modeled by intervals of utilities. Intervals of utilities may be mapped to singl... Read More about Comparing Intervals Using Type Reduction.

FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language (2020)
Conference Proceeding
Chen, C., Razak, T. R., & Garibaldi, J. M. (2020). FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-8). https://doi.org/10.1109/fuzz48607.2020.9177780

This paper presents an R package FuzzyR which is an extended fuzzy logic toolbox for the R programming language. FuzzyR is a continuation of the previous Fuzzy R toolboxes such as FuzzyToolkitUoN. Whilst keeping existing functionalities of the previo... Read More about FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language.

Just–In–Time Supply Chain Management Using Interval Type–2 Fuzzy Decision Making (2019)
Conference Proceeding
Runkler, T. A., Chen, C., Coupland, S., & John, R. (2019). Just–In–Time Supply Chain Management Using Interval Type–2 Fuzzy Decision Making. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/fuzz-ieee.2019.8858902

We propose the application of interval type-2 fuzzy decision making (IT2FDM) to dynamic scheduling of deliveries in a just-in-time logistic process. Delivery decisions are based on order priorities computed from the expected decrease of customer sati... Read More about Just–In–Time Supply Chain Management Using Interval Type–2 Fuzzy Decision Making.

Risk sensitive decision making using type reduction methods (2018)
Conference Proceeding
Runkler, T. A., Chen, C., & John, R. (in press). Risk sensitive decision making using type reduction methods. In Proceedings. 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018

A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm" (2018)
Journal Article
Chen, C., Wu, D., Garibaldi, J. M., John, R., Twycross, J., & Mendel, J. M. (2018). A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm". IEEE Transactions on Fuzzy Systems, 26(6), 3905-3907. https://doi.org/10.1109/tfuzz.2018.2865134

This letter is a supplement to the previous paper “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm”. In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most ineff... Read More about A comment on "A direct approach for determining the switch points in the Karnik-Mendel algorithm".

Type reduction operators for interval type–2 defuzzification (2018)
Journal Article
Runkler, T. A., Chen, C., & John, R. (2018). Type reduction operators for interval type–2 defuzzification. Information Sciences, 467, 464-476. https://doi.org/10.1016/j.ins.2018.08.023

Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set. Type–2 defuzzification maps type–2 fuzzy sets to n... Read More about Type reduction operators for interval type–2 defuzzification.

Interval Type–2 Defuzzification Using Uncertainty Weights (2017)
Book Chapter
Runkler, T. A., Coupland, S., John, R., & Chen, C. (2018). Interval Type–2 Defuzzification Using Uncertainty Weights. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (47-59). Cham: Springer Verlag. https://doi.org/10.1007/978-3-319-67789-7_4

© Springer International Publishing AG 2018. One of the most popular interval type–2 defuzzification methods is the Karnik–Mendel (KM) algorithm. Nie and Tan (NT) have proposed an approximation of the KM method that converts the interval type–2 membe... Read More about Interval Type–2 Defuzzification Using Uncertainty Weights.