ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS
Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in modelling of highly nonlinear components in the system. A laboratory set-up...
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iium-71202012-10-09T00:59:24Z http://irep.iium.edu.my/7120/ ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS Toha, Siti Fauziah Tokhi, M. O. TA Engineering (General). Civil engineering (General) Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in modelling of highly nonlinear components in the system. A laboratory set-up that resembles the behaviour of a helicopter, namely twin rotor multiinput multi-output system (TRMS) is used as an experimental rig in this research. An adaptive neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm is developed in search for non-parametric model for the TRMS. The antecedent parameters of the ANFIS are optimized by a PSO algorithm and the consequent parameters are updated using recursive least squares (RLS). The results show that the proposed technique has better convergence and better performance in modeling of a nonlinear process. The identified model is justified and validated in both time domain and frequency domain 2010 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/7120/1/05898130.pdf Toha, Siti Fauziah and Tokhi, M. O. (2010) ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS. In: 2010 IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS), 1 - 2 September 2010 , University of Reading Reading, U.K.. http://dx.doi.org/10.1109/UKRICIS.2010.5898130 doi:10.1109/UKRICIS.2010.5898130 |
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institution |
International Islamic University Malaysia |
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Online Access |
language |
English |
topic |
TA Engineering (General). Civil engineering (General) |
spellingShingle |
TA Engineering (General). Civil engineering (General) Toha, Siti Fauziah Tokhi, M. O. ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS |
description |
Artificial intelligence techniques, such as neural
networks and fuzzy logic have shown promising results for
modelling of nonlinear systems whilst traditional approaches are
rather insufficient due to difficulty in modelling of highly
nonlinear components in the system. A laboratory set-up that
resembles the behaviour of a helicopter, namely twin rotor multiinput multi-output system (TRMS) is used as an experimental rig
in this research. An adaptive neuro-fuzzy inference system
(ANFIS) tuned by particle swarm optimization (PSO) algorithm
is developed in search for non-parametric model for the TRMS.
The antecedent parameters of the ANFIS are optimized by a PSO
algorithm and the consequent parameters are updated using
recursive least squares (RLS). The results show that the proposed
technique has better convergence and better performance in
modeling of a nonlinear process. The identified model is justified
and validated in both time domain and frequency domain |
format |
Conference or Workshop Item |
author |
Toha, Siti Fauziah Tokhi, M. O. |
author_facet |
Toha, Siti Fauziah Tokhi, M. O. |
author_sort |
Toha, Siti Fauziah |
title |
ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS |
title_short |
ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS |
title_full |
ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS |
title_fullStr |
ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS |
title_full_unstemmed |
ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS |
title_sort |
anfis modelling of a twin rotor system using particle swarm optimisation and rls |
publishDate |
2010 |
url |
http://irep.iium.edu.my/7120/ http://irep.iium.edu.my/7120/ http://irep.iium.edu.my/7120/ http://irep.iium.edu.my/7120/1/05898130.pdf |
first_indexed |
2023-09-18T20:16:23Z |
last_indexed |
2023-09-18T20:16:23Z |
_version_ |
1777407816094449664 |