Open Access

ARTICLE

A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

by Aoqi Xu¹, Khalid A. Alattas², Nasreen Kausar³, Ardashir Mohammadzadeh⁴, Ebru Ozbilge^5,*, Tonguc Cagin⁵

1 School of Economics, Fujian Normal University, Fuzhou, 350007, China
2 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23890, Saudi Arabia
3 Department of Mathematics, Faculty of Arts and Science, Yildiz Technical University, Esenler, 34220, Istanbul, Turkey
4 Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China
5 American University of the Middle East, Department of Mathematics & Statistics, 54200, Egaila, Kuwait

* Corresponding Author: Ebru Ozbilge. Email:

(This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)

Intelligent Automation & Soft Computing 2023, 37(1), 17-32. https://doi.org/10.32604/iasc.2023.036623

Received 07 October 2022; Accepted 07 December 2022; Issue published 29 April 2023

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.

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Keywords

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