Identification of wiener fractional model using Self-Adaptive Velocity Particle Swarm Optimization

Lamia Sersour; Tounsia Djamah; Maamar Bettayeb

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$Identification of wiener fractional model using Self-Adaptive Velocity Particle Swarm Optimization$

Conference proceeding

Identification of wiener fractional model using Self-Adaptive Velocity Particle Swarm Optimization

Lamia Sersour, Tounsia Djamah and Maamar Bettayeb

The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1

01/01/2015

Abstract

Conference Title: 2015 7th International Conference on Modelling, Identification and Control (ICMIC) Conference Start Date: 2015, Dec. 18 Conference End Date: 2015, Dec. 20 Conference Location: Sousse, Tunisia This paper deals with identification of discrete nonlinear fractional order systems based on wiener models. Such systems consist of a linear dynamic block followed by a static non-linearity; in this study they are described using Polynomial Non Linear State Space(PNLSS) fractional models. Self Adaptive Velocity Particle Swarm Optimization (SAVPSO) is used; it is a modified PSO, which allows the constraints handling for solving constrained optimization problems (COPs). The wiener system identification is performed based on SAVPSO, and its efficiency is investigated on numerical simulations for different signal to noise rations.

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Title: Identification of wiener fractional model using Self-Adaptive Velocity Particle Swarm Optimization
Creators - without role: Lamia Sersour
Tounsia Djamah
Maamar Bettayeb
Publication Details: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Identifiers: 9937562608331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding