Type-2 Fuzzy Logic: A Historical View

John, Robert; Coupland, Simon

doi:10.1109/mci.2007.357194

Cited by 200 publications

(92 citation statements)

References 33 publications

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“…Unlike their T1 counterparts, whose membership values are precise numbers in the range [0, 1], membership grades of a T2 fuzzy set are themselves T1 fuzzy sets. Therefore, T2 fuzzy sets offer the ability to model higher levels of uncertainty (Mendel et al, 2006;John and Coupland, 2007). Aladi et al (2014) show how T2 fuzzy sets can handle increased noise and claim a direct relationship between FOU size and levels of noise.…”

Section: Anfis/t2 Modelsmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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Section: Anfis/t2 Modelsmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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“…Type-2 fuzzy sets [9] represent membership grades not as numbers in [0, 1], but as type-1 fuzzy sets. Type-2 fuzzy sets have been widely used in a number of applications (see [10] and [11] for examples), and on a number of problems T2FL has been shown to outperform T1FL (e.g., [1] and [2]). Some work has been done regarding the use of Evolutionary Algorithms to optimise Type-2 Fuzzy sets (e.g., [12] and [13]) however, in this work we do not optimise the sets; we use an Interval Type-2 fuzzy model as the means to evaluate resource plans, as this work focuses on optimising the latter.…”

Section: A Type-2 Fuzzy Logic (T2fl)mentioning

confidence: 99%

Inventory optimisation with an Interval Type-2 Fuzzy model

Miller

Góngora

John

2010

International Conference on Fuzzy Systems

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Abstract-The planning of resources within a supply chain can prove to be a deciding factor in the success or failure of an operation. This research continues the authors' previous work using an extended Interval Type-2 Fuzzy Logic supply chain model, with an Evolutionary Algorithm to search for good resource plans. A set of enhanced experiments is conducted to validate our novel approach with optimal configurations, and determine an appropriate Evolutionary Algorithm set up for the given problem.

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“…The fuzzy c-means (FCM) clustering [1] is a classical unsupervised soft clustering method and widely used in this domain because of its ability to handle fuzzy uncertainty. Classical FCM clustering methods are based on type-1 fuzzy set theory, which cannot address uncertainties associated with membership grade [2]. Some researchers adopt the interval type-2 fuzzy sets (IT2 FSs) to improve FCM clustering, and there are three strategies to avoid this problem: (1) The remote-sensing data expressed by real values are extended to interval numbers [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Guo

Huo

2017

Remote Sensing

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Abstract:Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEI sh ) is the best choice of spectral indices for this method.

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Type-2 Fuzzy Logic: A Historical View

Cited by 200 publications

References 33 publications

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Inventory optimisation with an Interval Type-2 Fuzzy model

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

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