Recursive Particle Swarm Optimization Application for the Radial Basis Function Networks Modeling System

Ling Liu; Min Chen; Hong Yi Guo

doi:10.4028/www.scientific.net/AMM.713-715.1817

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715Recursive Particle Swarm Optimization Application...

Recursive Particle Swarm Optimization Application for the Radial Basis Function Networks Modeling System

Abstract:

A Recursive Particle Swarm Optimization (RPSO) is proposed to solve dynamic optimization problems where the data is obtained not once but one by one. The position of each particle swarm is updated recursively based on the continuous data and the historical knowledge. The experiment results indicate that RPSO-based radial basis function networks needs fewer radial basis functions and gives more accurate results than traditional PSO in solving dynamic problems.

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Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

1817-1820

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.1817

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Online since:

January 2015

Authors:

Ling Liu, Min Chen*, Hong Yi Guo

Keywords:

Dynamic optimization, Radial Basis Function Networks Modeling System, Recursive Estimation, Recursive Particle Swarm Optimization

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References

[1] KENNEDY J, EBERHART R. Particle swarm optimization[C]/Proc of IEEE International Conference on Neural Networks. New Jersey: IEEE Press, 1995: 1942-(1948).

Google Scholar

[2] OCA M A M, STUZLE T, ENDEN V K, et al. Incremental social learning in particle swarms[J]. IEEE Trans Syst Man Cybern B Cybern, 2011, 41(2): 368-384.

DOI: 10.1109/tsmcb.2010.2055848

Google Scholar

[3] SHELOKAR P S, SIARRY P, JAYARAMAN V K, et al. Particleswarm and antcolony algorithms hybridized for improved continuous optimization[J]. Applied Mathematics and Computation, 2007, 18: 129-142.

DOI: 10.1016/j.amc.2006.09.098

Google Scholar

[4] KENNEDY J, EBERHART R. A new optimizer using particle swarm theory[C]/Proceedings of the Sixth International Symposium. Nagoya: Micromachine and Human Science, 1995, 3: 39-43.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[5] WANG Y, PAN H, ZHAO B. Application of radial basis function network in the fault diagnosis of enginep[J]. Microcomputer Information, 2009, 25(1): 183-185.

Google Scholar

[6] JIAO B, LIAN Z, GU X. A dynamic inertia weight particle swarm optimization algorithm[J]. Chaos Solitons & Fractals, 2008, 37(3): 698-705.

DOI: 10.1016/j.chaos.2006.09.063

Google Scholar

[7] FENG H, CHEN C. Automatic hybrid particle swarm optimization recursive clustering technique and its applications in radial basis function networks modeling systems[C]/Neuro Computing Research Developments. New York: Nova Science Publishers Inc, 2007: 201-222.

Google Scholar