Numerical Solution of Fuzzy Equations with Z-numbers using Neural Networks

被引：19
|
作者：
Jafari, Raheleh [1 ]
Yu, Wen [1 ]
Li, Xiaoou [2 ]
机构：
[1] Natl Polytech Inst, CINVESTAV IPN, Dept Control Automat, Mexico City, DF, Mexico
[2] Natl Polytech Inst, CINVESTAV IPN, Dept Computat, Mexico City, DF, Mexico
来源：
INTELLIGENT AUTOMATION AND SOFT COMPUTING | 2018年 / 24卷 / 01期
关键词：
Fuzzy equation; Z-number; Fuzzy control; FRACTIONAL DIFFERENTIAL-EQUATIONS; INITIAL-VALUE PROBLEM; NONLINEAR EQUATIONS; GENERALIZED THEORY; ITERATIVE METHOD; UNCERTAINTY GTU; SYSTEMS; IDENTIFICATION;
D O I：
10.1080/10798587.2017.1327154
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the uncertainty property is represented by the Z-number as the coefficients of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. We also extend the fuzzy equation into dual type, which is natural for linearin-parameter nonlinear systems. The solutions of these fuzzy equations are the controllers when the desired references are regarded as the outputs.The existence conditions of the solutions (controllability) are proposed. Two types of neural networks are implemented to approximate solutions of the fuzzy equations with Z-number coefficients.
引用
收藏
页码：151 / 157
页数：7
相关论文
共 50 条
  • [1] Numerical Solution of Fuzzy Differential Equations with Z-numbers Using Bernstein Neural Networks
    Raheleh Jafari
    Wen Yu
    Xiaoou Li
    Sina Razvarz
    [J]. International Journal of Computational Intelligence Systems, 2017, 10 : 1226 - 1237
  • [2] Numerical Solution of Fuzzy Differential Equations with Z-numbers Using Bernstein Neural Networks
    Jafari, Raheleh
    Yu, Wen
    Li, Xiaoou
    Razvarz, Sina
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2017, 10 (01) : 1226 - 1237
  • [3] Neural Network Approach to Solving Fuzzy Nonlinear Equations Using Z-Numbers
    Jafari, Raheleh
    Razvarz, Sina
    Gegov, Alexander
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 28 (07) : 1230 - 1241
  • [4] Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers
    Jafari, Raheleh
    Razvarz, Sina
    Gegov, Alexander
    Paul, Satyam
    [J]. ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI), 2019, 840 : 96 - 107
  • [5] Trust Prediction Using Z-numbers and Artificial Neural Networks
    Azadeh, Ali
    Kokabi, Reza
    Saberi, Morteza
    Hussain, Farookh Khadeer
    Hussain, Omar Khadeer
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2014, : 522 - 528
  • [6] Fuzzy equations and Z-numbers for nonlinear systems control
    Razvarz, Sina
    Tahmasbi, Mohammad
    [J]. 9TH INTERNATIONAL CONFERENCE ON THEORY AND APPLICATION OF SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTION, ICSCCW 2017, 2017, 120 : 923 - 930
  • [7] Numerical Solution of Linear Regression Based on Z-Numbers by Improved Neural Network
    Ezadi, Somayeh
    Allahviranloo, Tofigh
    [J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2018, 24 (01): : 193 - 203
  • [8] Solving Differential Equations with Z-Numbers by Utilizing Fuzzy Sumudu Transform
    Jafari, Raheleh
    Razvarz, Sina
    Gegov, Alexander
    [J]. INTELLIGENT SYSTEMS AND APPLICATIONS, INTELLISYS, VOL 2, 2019, 869 : 1125 - 1138
  • [9] Fuzzy Delphi Method with Z-Numbers
    Lawnik, Marcin
    Krakowczyk, Joanna
    Banasik, Arkadiusz
    [J]. INFORMATION AND SOFTWARE TECHNOLOGIES, ICIST 2019, 2019, 1078 : 24 - 32
  • [10] Uncertain non near system control with Fuzzy Differential Equations and Z-numbers
    Jafari, Raheleh
    Yu, Wen
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2017, : 749 - 754
12345