@Article{iasc.2023.036623,
AUTHOR = {Aoqi Xu, Khalid A. Alattas, Nasreen Kausar, Ardashir Mohammadzadeh, Ebru Ozbilge, Tonguc Cagin},
TITLE = {A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {1},
PAGES = {17--32},
URL = {http://www.techscience.com/iasc/v37n1/52637},
ISSN = {2326-005X},
ABSTRACT = {In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs.
noisy environments. The primary objective of this study is to present a more
robust method against uncertainties. This paper proposes a new deep learning
scheme for modeling and identification applications. The suggested approach is
based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support
measurement errors and high-level uncertainties. Besides the rule optimization,
the antecedent parameters and the level of secondary memberships are also
adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear
structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling
scheme is verified. The main advantages of the suggested approach include better
resistance against uncertainties, deep learning, and good convergence.},
DOI = {10.32604/iasc.2023.036623}
}