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62781

Tuning the Parameters of TSK Neuro-Fuzzy System by Particle Swarm Optimization

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Last updated: 25 Dec 2024

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Abstract

Particle Swarm Optimization (PSO) algorithm is applied to improve the efficiency of Takagi-Sugeno-Kang (TSK) neuro-fuzzy network in identification of nonlinear system. First, a TSK type neuro-fuzzy system is adopted for improving identification and prediction, and then PSO technique is adopted to optimize the execution of neuro-fuzzy network. The simulation results indicate that the applied PSO accomplishes good performance and tracks the plant output with minimal error.

DOI

10.21608/mjeer.2019.62781

Authors

First Name

Sally

Last Name

Abdulaziz

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Affiliation

Dept. of Industrial Elect. and Control Eng., Faculty of Elect., Eng., Menoufia University, Egypt.

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First Name

Essam

Last Name

Nabil

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Affiliation

Dept. of Industrial Elect. and Control Eng., Faculty of Elect., Eng., Menoufia University, Egypt.

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First Name

Gomaa

Last Name

Zaki

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-

Affiliation

Dept. of Industrial Elect. and Control Eng., Faculty of Elect., Eng., Menoufia University, Egypt.

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-

Orcid

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First Name

Galal

Last Name

Atlam

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-

Affiliation

Dept. of Industrial Elect. and Control Eng., Faculty of Elect., Eng., Menoufia University, Egypt.

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Volume

28

Article Issue

2

Related Issue

9507

Issue Date

2019-07-01

Receive Date

2018-10-30

Publish Date

2019-07-01

Page Start

245

Page End

258

Print ISSN

1687-1189

Online ISSN

2682-3535

Link

https://mjeer.journals.ekb.eg/article_62781.html

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https://mjeer.journals.ekb.eg/service?article_code=62781

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14

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Original Article

Type Code

1,088

Publication Type

Journal

Publication Title

Menoufia Journal of Electronic Engineering Research

Publication Link

https://mjeer.journals.ekb.eg/

MainTitle

Tuning the Parameters of TSK Neuro-Fuzzy System by Particle Swarm Optimization

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Article

Created At

22 Jan 2023