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A Non-Singleton type-3 fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

Intelligent Automation & Soft Computing 2023年第7期37卷 17-32页

作者： Aoqi Xu Khalid A.Alattas Nasreen Kausar Ardashir Mohammadzadeh Ebru Ozbilge Tonguc Cagin School of Economics Fujian Normal UniversityFuzhou350007China Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenler34220IstanbulTurkey Multidisciplinary Center for Infrastructure Engineering Shenyang University of TechnologyShenyang110870China American University of the Middle East Department of Mathematics&Statistics54200EgailaKuwait

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identiﬁ*** main gap is the weakness of current methods *** *** primary objective of this study is to present a more robust method against *** paper proposes a new deep learning scheme for modeling and identiﬁcation *** suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level *** the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanﬁlter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is *** designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data *** various ana-lyses and comparisons,the better efﬁciency of the proposed fuzzy modeling scheme is veriﬁ*** main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

关键词： Modeling computational intelligence fuzzy logic systems modeling identiﬁcation deep learning type-3 fuzzy systems optimization

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