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Adaptive non-singleton type-2 fuzzy logic systems: A way forward for handling numerical uncertainties in real world applications

Sahab, N and Hagras, H (2011) 'Adaptive non-singleton type-2 fuzzy logic systems: A way forward for handling numerical uncertainties in real world applications.' International Journal of Computers, Communications and Control, 6 (3). 503 - 529. ISSN 1841-9836


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Real world environments are characterized by high levels of lin-guistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision avail-able in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applica-tions such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first gen-eration of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncer-tainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numer-ical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents' linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties. © 2006-2011 by CCC Publications.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 13 May 2013 19:49
Last Modified: 23 Jan 2019 00:17

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