A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size
In this study, a self-organizing interval type-3 fuzzy logic system (SO-IT3FLS) with a new learning algorithm is presented. An adaptive kernel size using fuzzy systems is introduced to improve the robustness of conventional correntropy based Kalman filters against non-Gaussian noise. The maximum cor...
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| Veröffentlicht in: | Information sciences Jg. 572; S. 424 - 443 |
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01.09.2021
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| Abstract | In this study, a self-organizing interval type-3 fuzzy logic system (SO-IT3FLS) with a new learning algorithm is presented. An adaptive kernel size using fuzzy systems is introduced to improve the robustness of conventional correntropy based Kalman filters against non-Gaussian noise. The maximum correntropy Kalman filter (MCKF) and maximum correntropy unscented Kalman filter (MCUKF) with the proposed adaptive fuzzy kernel size are reformulated to optimize both rule and antecedent parameters, respectively. In addition to the rule parameters, the proposed membership function (MF) parameters and the level of α-cuts are also optimized. Five simulation examples with real-world data sets are given for examination. The simulations show that the introduced SO-IT3FLS and learning algorithm result in better accuracy in contrast to the other kind of fuzzy neural networks and conventional learning techniques. Furthermore, it is verified that the robustness of the proposed learning method against non-Gaussian noise is improved in contrast to the conventional Kalman filter, maximum correntropy Kalman filter and unscented Kalman filter. |
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| AbstractList | In this study, a self-organizing interval type-3 fuzzy logic system (SO-IT3FLS) with a new learning algorithm is presented. An adaptive kernel size using fuzzy systems is introduced to improve the robustness of conventional correntropy based Kalman filters against non-Gaussian noise. The maximum correntropy Kalman filter (MCKF) and maximum correntropy unscented Kalman filter (MCUKF) with the proposed adaptive fuzzy kernel size are reformulated to optimize both rule and antecedent parameters, respectively. In addition to the rule parameters, the proposed membership function (MF) parameters and the level of α-cuts are also optimized. Five simulation examples with real-world data sets are given for examination. The simulations show that the introduced SO-IT3FLS and learning algorithm result in better accuracy in contrast to the other kind of fuzzy neural networks and conventional learning techniques. Furthermore, it is verified that the robustness of the proposed learning method against non-Gaussian noise is improved in contrast to the conventional Kalman filter, maximum correntropy Kalman filter and unscented Kalman filter. |
| Author | Pahlevanzadeh, Bahareh Ahmadian, Ali Qasem, Sultan Noman Mohammadzadeh, Ardashir Rathinasamy, Sakthivel |
| Author_xml | – sequence: 1 givenname: Sultan Noman surname: Qasem fullname: Qasem, Sultan Noman email: SNMohammed@imamu.edu.sa organization: Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia – sequence: 2 givenname: Ali surname: Ahmadian fullname: Ahmadian, Ali email: ahmadian@ubonab.ac.ir organization: Electrical Engineering Department, University of Bonab, Bonab, Iran – sequence: 3 givenname: Ardashir surname: Mohammadzadeh fullname: Mohammadzadeh, Ardashir email: a.mzadeh@ubonab.ac.ir organization: Electrical Engineering Department, University of Bonab, Bonab, Iran – sequence: 4 givenname: Sakthivel surname: Rathinasamy fullname: Rathinasamy, Sakthivel organization: Department of Applied Mathematics, Bharathiar University, Coimbatore 641-046, India – sequence: 5 givenname: Bahareh surname: Pahlevanzadeh fullname: Pahlevanzadeh, Bahareh email: pahlevanzadeh@ricest.ac.ir organization: Department of Design and System Operations, Regional Information Center for Science and Technology (RICeST), Shiraz, Fars, Iran |
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| Keywords | Interval type-3 fuzzy logic systems Self-organizing Correntropy criterion Learning algorithm Kalman filter |
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