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

作　　者：Aoqi Xu Khalid A.Alattas Nasreen Kausar Ardashir Mohammadzadeh Ebru Ozbilge Tonguc Cagin 

机构地区：[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

出　　处：《Intelligent Automation & Soft Computing》2023年第7期17-32,共16页智能自动化与软计算（英文）

基　　金：supported by the project of the National Social Science Fundation(21BJL052,20BJY020,20BJL127,19BJY090);the 2018 Fujian Social Science Planning Project(FJ2018B067);The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019(19YJA790102),The grant has been received by Aoqi Xu.

摘　　要：In many problems,to analyze the process/metabolism behavior,a mod-el 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 Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses 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.

关 键 词：MODELING computational intelligence fuzzy logic systems MODELING identification deep learning type-3 fuzzy systems optimization 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]