Intelligent Optimization Algorithm for Nonlinear Function

Author

Guo, Jian ; Gong, Jing

Author_Institution

Coll. of Civil Eng., Wuhan Polytech. Univ., Wuhan, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Original particle swarm optimization (OPSO) algorithm was modified in the paper, and a self-adaptive PSO (SPSO) was proposed. In this algorithm, SPSO combines Elman neural network (ENN) and forms SPSO-ENN hybrid algorithm. Compared with ENN algorithm, the experiment results show that SPSO-ENN has less adjustable parameters, faster convergence speed and higher precision in the nonlinear function identification.

Keywords

neural nets; nonlinear functions; particle swarm optimisation; self-adjusting systems; Elman neural network; SPSO-ENN hybrid algorithm; faster convergence speed; intelligent optimization algorithm; less adjustable parameter; nonlinear function identification; particle swarm optimization; self-adaptive PSO; Artificial intelligence; Artificial neural networks; Convergence; Feedforward neural networks; Feedforward systems; Intelligent networks; Neural networks; Nonlinear dynamical systems; Particle swarm optimization; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5365069

Filename

5365069