ANFIS: General description for modeling dynamic objects

M. Alakhras; M. Oussalah; M. Hussein

doi:10.1109/AICCSA.2015.7507102

ANFIS: General description for modeling dynamic objects

M. Alakhras, M. Oussalah, M. Hussein

Department of Electrical and Communication Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as "neuro-fuzzy". This combination seems natural because the two approaches generally attack the design of "intelligent" systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems.

Original language	English
Journal	Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume	2016-January
DOIs	https://doi.org/10.1109/AICCSA.2015.7507102
Publication status	Published - 2016
Event	12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2015 - Marrakech, Morocco Duration: Nov 17 2015 → Nov 20 2015

Keywords

Adaptive Systems (ANFIS)
Neuro-Fuzzy (NF), Modeling of Nonlinear Objects
Soft Computing (sq)

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Signal Processing
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/AICCSA.2015.7507102

Cite this

@article{8ebeed34f15d41dd96303e9adadbb981,

title = "ANFIS: General description for modeling dynamic objects",

abstract = "This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as {"}neuro-fuzzy{"}. This combination seems natural because the two approaches generally attack the design of {"}intelligent{"} systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems.",

keywords = "Adaptive Systems (ANFIS), Neuro-Fuzzy (NF), Modeling of Nonlinear Objects, Soft Computing (sq)",

author = "M. Alakhras and M. Oussalah and M. Hussein",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE; 12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2015 ; Conference date: 17-11-2015 Through 20-11-2015",

year = "2016",

doi = "10.1109/AICCSA.2015.7507102",

language = "English",

volume = "2016-January",

journal = "Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA",

issn = "2161-5322",

}

TY - JOUR

T1 - ANFIS

T2 - 12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2015

AU - Alakhras, M.

AU - Oussalah, M.

AU - Hussein, M.

PY - 2016

Y1 - 2016

N2 - This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as "neuro-fuzzy". This combination seems natural because the two approaches generally attack the design of "intelligent" systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems.

AB - This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as "neuro-fuzzy". This combination seems natural because the two approaches generally attack the design of "intelligent" systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems.

KW - Adaptive Systems (ANFIS)

KW - Neuro-Fuzzy (NF), Modeling of Nonlinear Objects

KW - Soft Computing (sq)

UR - http://www.scopus.com/inward/record.url?scp=85115448174&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85115448174&partnerID=8YFLogxK

U2 - 10.1109/AICCSA.2015.7507102

DO - 10.1109/AICCSA.2015.7507102

M3 - Conference article

AN - SCOPUS:85115448174

SN - 2161-5322

VL - 2016-January

JO - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA

JF - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA

Y2 - 17 November 2015 through 20 November 2015

ER -

ANFIS: General description for modeling dynamic objects

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this