Finishing mill strip gage setup and control by interval type-1 non-singleton type-2 fuzzy logic systems

Mendez, Gerardo M.; Castillo, Oscar; Colás, Rafael; Moreno, Héctor

doi:10.1016/j.asoc.2014.08.052

Cited by 19 publications

(4 citation statements)

References 41 publications

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“…Further, non-singleton inputs have also been applied to type-2 FLSs, resulting in hybrid solutions for uncertainty handling at different uncertainty levels. For example in [28] and [29], non-singleton type-2 FLSs are shown to effectively handle different levels of uncertainties in industrial use cases. It has also been shown that in the presence of perturbations, the generalized type-2 fuzzy controllers outperform their type-1 and interval type-2 counterparts [30], [31], and that interval type-2 NSFLSs outperform type-1 NSFLSs in different application domains [32], [33].…”

Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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(2016) Improved uncertainty capture for non-singleton fuzzy systems. IEEE Transactions on Fuzzy Systems, 24 (6). pp. 1513 -1524 . ISSN 1941 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/45444/1/Improved%20Uncertainty%20Capture%20for %20Non-Singleton%20Fuzzy%20Systems.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 Improved Uncertainty Capture for Non-Singleton Fuzzy SystemsAmir Pourabdollah, Member, IEEE, Christian Wagner, Senior Member, IEEE, Jabran Hussain Aladi, Student Member, IEEE and Jonathan M. Garibaldi, Member, IEEE Abstract-In non-singleton fuzzy logic systems (NSFLSs), input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both: the appropriate modelling in the input fuzzy sets of the uncertainties present in the system's inputs, and on how the input fuzzy sets (and their inherent model of uncertainty) interact with the antecedent and thus affect the inference within the remainder of the NSFLS. This paper proposes a novel development on the latter. Specifically, an alteration to the standard composition method of type-1 fuzzy relations is proposed, and applied to build a new type of NSFLS. The proposed approach is based on employing the centroid of the intersection of input and antecedent sets as origin of the firing degree, rather than the traditional maximum of their intersection, thus making the NSFLS more sensitive to changes in the input's uncertainty characteristics. The traditional and novel approach to NSFLSs are experimentally compared for two well-known problems of Mackey-Glass and Lorenz chaotic time series predictions, where the NSFLSs' inputs have been perturbed with different levels of Gaussian noise. Experiments are repeated for system training under noisy and noise-free conditions. Analyses of the results show that the new method outperforms the traditional approach. Moreover, it is shown that while formally more complex, in practice, the new method has no significant computational overhead compared to the standard approach.

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Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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“…Steepest-descent method, which is referred to as recursive back-propagation mechanism, has been proposed in [37] to adjust the parameters of proposed type-2 fuzzy system. A review on the techniques used in the optimization of type-2 fuzzy controllers has been considered in [38].…”

Section: Introductionmentioning

confidence: 99%

A new robust observer-based adaptive type-2 fuzzy control for a class of nonlinear systems

Mohammadzadeh

Hashemzadeh

2015

Applied Soft Computing

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“…To do this, an X-ray gauge sensor was linked to an IT2 FLC [64]. Additional inputs are the transfer bar thickness, the entry width, the entry temperature, the target thickness, the output width, the output temperature, the standing work roll diameter, the work roll speed, the stand entry thickness, the stand exit thickness, the stand rolling force and the percent carbon of the strip.…”

Section: It2mentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

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Finishing mill strip gage setup and control by interval type-1 non-singleton type-2 fuzzy logic systems

Cited by 19 publications

References 41 publications

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

A new robust observer-based adaptive type-2 fuzzy control for a class of nonlinear systems

Review of Recent Type-2 Fuzzy Controller Applications

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