New Concepts Related to Non-Stationary Fuzzy Sets

Garibaldi, Jonathan M.; Jaroszewski, Marcin; Musikasuwan, S.

doi:10.1109/fuzzy.2007.4295619

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…n instantiations of an ordinary FIS). For more details, including elementary theoretical properties of non-stationary fuzzy sets, see [12], [14], [15].…”

Section: Non-stationary Fuzzy Sets and Systemsmentioning

confidence: 99%

Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer

Wang

Garibaldi

Zhou

et al. 2009

2009 IEEE International Conference on Fuzzy Systems

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Recommending appropriate follow-up treatment op tions to patients after diagnosis and primary (usually surgi cal) treatment of breast cancer is a complex decision making problem. Often, the decision is reached by consensus from a multi-disciplinary team of oncologists, radiologists, surgeons and pathologists. Non-stationary fuzzy sets have been proposed as a mechanism to represent and reason with the knowledge of such multiple experts. In this paper, we briefly describe the creation of a non-stationary fuzzy inference system to provide decision support in this context, and examine a number of alternative methods for interpreting the output of such a non-stationary inference system. The alternative interpretation methodologies and the experiments carried out to compare these methods are detailed. Results are presented which shown that using majority voting ensemble decision making from a non-stationary fuzzy system improves accuracy of the decision making. We conclude that non-stationary systems coupled with ensemble interpretation methods are worthy of further exploration.

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“…n instantiations of an ordinary FIS). For more details, including elementary theoretical properties of non-stationary fuzzy sets, see [12], [14], [15].…”

Section: Non-stationary Fuzzy Sets and Systemsmentioning

confidence: 99%

Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer

Wang

Garibaldi

Zhou

et al. 2009

2009 IEEE International Conference on Fuzzy Systems

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“…Nonstationary fuzzy sets were first proposed by Garibaldi et al [12], [13], [14] in order to capture the variation facet of uncertainty within a fuzzy set. Many fuzzy concepts are subject to slight variations with time or other latent variables.…”

Section: Nonstationary Fuzzy Setsmentioning

confidence: 99%

Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules

Coupland

Matthews

2013

2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ)

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Abstract-Modern organisations now collect very large volumes of data about customers, suppliers and other factors which may impact upon their business. There is a clear need to be able to mine this data and present it to decision makers in a clear and coherent manner. Fuzzy association rules are a popular method to identifying important and meaningful relationships within large data sets. Recently a fuzzy association rule has been proposed that uses the 2-tuple linguistic representation. This paper presents a methodology which makes use of nonstationary fuzzy sets to post process 2-tuple fuzzy association rules reducing the size of the mined rule set by around 20% whilst retaining the semantic meaning of the rule set.

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“…In addition, type-2 fuzzy elliptic functions do not explain the concept of variability and cannot model the diversity of opinions of a single expert or a group of experts. The nonstationary fuzzy set theory and the corresponding nonstationary fuzzy inference system were introduced [16].…”

Section: Introductionmentioning

confidence: 99%

“…(1) An elliptic type-2 function (ET2F) [14], [15], [16], [17] along with a Takagi-Sugeno-Kang fuzzy system [18], [19] and a brain imitated neural network VOLUME 11, 2023 (BINN) [24] are incorporated to improve response to input uncertainties. (2) The updated weight of the TSK application is based on the wavelet function, which increases the flexibility of the automatic parameter updating [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Design of Missile Guidance Law Using Takagi-Sugeno-Kang (TSK) Elliptic Type-2 Fuzzy Brain Imitated Neural Networks

et al. 2023

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The exploiting of missile guidance law is one of the most important issue of defense systems. However, it is a complex nonlinear guidance control problem. In recent years, several intelligent algorithms have been employed for the missile guidance law design. This paper aims to provide a more effective intelligent neural network, and then apply it to missile guidance control. An elliptic type-2 function (ET2F) combined with a Takagi-Sugeno-Kang (TSK) fuzzy system (FS) based on a wavelet function and a brain imitated neural network (BINN) is proposed to produce a new Takagi-Sugeno-Kang elliptic type-2 fuzzy brain imitated neural network (TSKET2FBINN). The proposed TSKET2FBINN is then incorporated into the missile guidance law. The proposed missile guidance control system includes the TSKET2FBINN controller and a robust compensation controller. A Lyapunov function is used to generate parameter adjustment adaptive laws so as to guarantee system stability with fast tracking performance of missile guidance control system. By presenting three missile control scenarios, the effectiveness of the proposed method is then demonstrated. The miss-distance (MD) of the proposed method is calculated and compared to other recent guidance methods to demonstrate its superior performance.INDEX TERMS Elliptic type-2 function, TSK fuzzy system, brain imitated neural network, missile guidance law, command to line-of-sight.

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New Concepts Related to Non-Stationary Fuzzy Sets

Cited by 11 publications

References 7 publications

Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer

Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer

Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules

Design of Missile Guidance Law Using Takagi-Sugeno-Kang (TSK) Elliptic Type-2 Fuzzy Brain Imitated Neural Networks

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