Intuitionistic Neo-Fuzzy Network for modeling of nonlinear systems dynamics

Margarita Terziyska; Yancho Todorov

doi:10.1109/IS.2016.7737491

Intuitionistic Neo-Fuzzy Network for modeling of nonlinear systems dynamics

Margarita Terziyska
, Yancho Todorov

Not published at Aalto University

University of Food Technologies-Plovdiv
Bulgarian Academy of Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

In this paper, an approach to design an Intuitionistic Neo-Fuzzy Network (INFN) is presented. The proposed architecture combines the advantages of the Intuitionistic Fuzzy Logic (IFL) to deal with uncertainties and the Neo-Fuzzy Neural Network approach to represent nonlinear systems with topologies including small number of parameters. As a learning approach for the consequent fuzzy rules parameters, the gradient optimization procedure is proposed. The investigate the potentials of the generated INF structure, the modeling of a three benchmark chaotic time series - Mackey-Glass, Lorenz and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its further extension to Model Predictive Control is investigated too.

Original language	English
Title of host publication	2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings
Publisher	IEEE
Pages	616-621
Number of pages	6
ISBN (Electronic)	978-1-5090-1354-8
DOIs	https://doi.org/10.1109/IS.2016.7737491
Publication status	Published - 7 Nov 2016
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Intelligent Systems - Sofia, Bulgaria Duration: 4 Sept 2016 → 6 Sept 2016 Conference number: 8

Conference

Conference	IEEE International Conference on Intelligent Systems
Abbreviated title	IS
Country/Territory	Bulgaria
City	Sofia
Period	04/09/2016 → 06/09/2016

Keywords

chaotic time series
Intuitionistic Fuzzy Sets
Lorenz time series
Mackey-Glass time series
Model Predictive Control
Modelling
Neo-Fuzzy Network
Prediction
Rossler time series

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10.1109/IS.2016.7737491

Cite this

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title = "Intuitionistic Neo-Fuzzy Network for modeling of nonlinear systems dynamics",

abstract = "In this paper, an approach to design an Intuitionistic Neo-Fuzzy Network (INFN) is presented. The proposed architecture combines the advantages of the Intuitionistic Fuzzy Logic (IFL) to deal with uncertainties and the Neo-Fuzzy Neural Network approach to represent nonlinear systems with topologies including small number of parameters. As a learning approach for the consequent fuzzy rules parameters, the gradient optimization procedure is proposed. The investigate the potentials of the generated INF structure, the modeling of a three benchmark chaotic time series - Mackey-Glass, Lorenz and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its further extension to Model Predictive Control is investigated too.",

keywords = "chaotic time series, Intuitionistic Fuzzy Sets, Lorenz time series, Mackey-Glass time series, Model Predictive Control, Modelling, Neo-Fuzzy Network, Prediction, Rossler time series",

author = "Margarita Terziyska and Yancho Todorov",

year = "2016",

month = nov,

day = "7",

doi = "10.1109/IS.2016.7737491",

language = "English",

pages = "616--621",

booktitle = "2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Intelligent Systems, IS ; Conference date: 04-09-2016 Through 06-09-2016",

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AU - Terziyska, Margarita

AU - Todorov, Yancho

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Y1 - 2016/11/7

N2 - In this paper, an approach to design an Intuitionistic Neo-Fuzzy Network (INFN) is presented. The proposed architecture combines the advantages of the Intuitionistic Fuzzy Logic (IFL) to deal with uncertainties and the Neo-Fuzzy Neural Network approach to represent nonlinear systems with topologies including small number of parameters. As a learning approach for the consequent fuzzy rules parameters, the gradient optimization procedure is proposed. The investigate the potentials of the generated INF structure, the modeling of a three benchmark chaotic time series - Mackey-Glass, Lorenz and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its further extension to Model Predictive Control is investigated too.

AB - In this paper, an approach to design an Intuitionistic Neo-Fuzzy Network (INFN) is presented. The proposed architecture combines the advantages of the Intuitionistic Fuzzy Logic (IFL) to deal with uncertainties and the Neo-Fuzzy Neural Network approach to represent nonlinear systems with topologies including small number of parameters. As a learning approach for the consequent fuzzy rules parameters, the gradient optimization procedure is proposed. The investigate the potentials of the generated INF structure, the modeling of a three benchmark chaotic time series - Mackey-Glass, Lorenz and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its further extension to Model Predictive Control is investigated too.

KW - chaotic time series

KW - Intuitionistic Fuzzy Sets

KW - Lorenz time series

KW - Mackey-Glass time series

KW - Model Predictive Control

KW - Modelling

KW - Neo-Fuzzy Network

KW - Prediction

KW - Rossler time series

UR - https://www.scopus.com/pages/publications/85006043215

U2 - 10.1109/IS.2016.7737491

DO - 10.1109/IS.2016.7737491

M3 - Conference article in proceedings

AN - SCOPUS:85006043215

SP - 616

EP - 621

BT - 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings

PB - IEEE

T2 - IEEE International Conference on Intelligent Systems

Y2 - 4 September 2016 through 6 September 2016

ER -

Intuitionistic Neo-Fuzzy Network for modeling of nonlinear systems dynamics

Abstract

Conference

Keywords

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