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Algorithms 2017, 10(3), 99; doi:10.3390/a10030099

Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

1
School of Engineering, Universidad Autonoma de Baja California, Tijuana 22390, Mexico
2
Division of Graduate Studies, Tijuana Institute of Technology, Tijuana 22414, Mexico
*
Author to whom correspondence should be addressed.
Received: 19 June 2017 / Revised: 22 August 2017 / Accepted: 23 August 2017 / Published: 25 August 2017
(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)
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Abstract

This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty in the system, which in turn is used to define the parameters in the interval type-2 TSK linear functions via a dual LSE algorithm. Multiple classification benchmark datasets were tested in order to assess the quality of the formed granular models; its performance is also compared against other common classification algorithms. Shown results conclude that classification performance in general is better than results obtained by other techniques, and in general, all achieved results, when averaged, have a better performance rate than compared techniques, demonstrating the stability of the proposed hybrid learning technique. View Full-Text
Keywords: general type-2 fuzzy logic system; TSK; hybrid learning; principle of justifiable granularity; information granule; classification; granular computing general type-2 fuzzy logic system; TSK; hybrid learning; principle of justifiable granularity; information granule; classification; granular computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sanchez, M.A.; Castro, J.R.; Ocegueda-Miramontes, V.; Cervantes, L. Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems. Algorithms 2017, 10, 99.

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