Identification for fractional order rational models based on particle swarm optimisation

Authors:
Defeng Wu;Zi Ma;Aiguo Li;Quanmin Zhu
Affiliations:
Automation Research Centre, Dalian Maritime University, Dalian 116026, China.;Automation Research Centre, Dalian Maritime University, Dalian 116026, China.;Automation Research Centre, Dalian Maritime University, Dalian 116026, China.;Faculty of Computing, Engineering and Mathematical Sciences (CEMS), University of the West of England (UWE), Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, UK
Venue:
International Journal of Computer Applications in Technology
Year:
2011

Citing 3
Cited 5

Structure identification of nonlinear dynamic systems—a survey on input/output approaches

Automatica (Journal of IFAC)
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

IEEE Transactions on Evolutionary Computation
An alternative solution to the model structure selection problem

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

A hybrid particle swarm optimisation with social weight for non-convex economic dispatch problem

International Journal of Computer Applications in Technology
An enhanced differential evolution algorithm for solving large scale optimisation problems on graphics hardware

International Journal of Computer Applications in Technology
Improved bee colony algorithm based on knowledge strategy for digital filter design

International Journal of Computer Applications in Technology
Bacterial foraging based moon symmetry axis estimation for spacecraft attitude determination

International Journal of Computer Applications in Technology
A self-organisation particle swarm optimisation algorithm based on L norm multi-measurements diversity feedback

International Journal of Computer Applications in Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Particle swarm optimisation (PSO) algorithm for estimating the parameters of non-linear fractional order rational models is proposed in this paper. The effectiveness of the PSO estimator is initially demonstrated by three examples including a real-time case study.