A nonlinear system modeling method based on projection pursuit and particle swarm optimization algorithm

Document Type : Research Paper


Ninevah University, Iraq


Based on projection pursuit and particle swarm algorithm, a new method of establishing a nonlinear system model and its algorithm implementation are proposed, and two simulation examples are given. The simulation results show that the method proposed in this paper is used to establish a nonlinear system model. It has the advantages of high prediction accuracy and good engineering practicability. 


[1] A. Berro, S.L. Marie-Sainte and A. Ruiz-Gazen, Genetic algorithms and particle swarm optimization for exploratory projection pursuit, Ann. Math. Artif. Intell. 60 (2010), no. 1, 153–178.
[2] J.H. Friedman and W. Stuetzle, Projection pursuit regression, J. Amer. Statist. Assoc. 76 (1981), 817–823.
[3] J.H. Friedman and J. W. Tukey, A projection pursuit algorithm for exploratory data analysis, IEEE Trans.
Comput. 23 (1974), 881–889.
[4] Q. Jiang, Q. Fu and Z. Wang, Comprehensive evaluation of regional land resources carrying capacity based on
projection pursuit model optimized by particle swarm optimization, Trans. Chinese Soc. Agric. Engin. 27 (2011),
no. 11, 319–324.
[5] Z. Li, X. Deng and Y. Xin, Model for drought and flood trend prediction using projection pursuit regression, J.
Natural Disasters 6 (1997), no. 4, 68–73.
[6] Z. Li, The PPR model for pollutant concentration prediction, Environ. Sci. 18 (1997), no. 4, 38–40.
[7] J. Li and Q. Chen, The construction of a continuously changeable base of n2demensional space and projection
pursuit, J. Guizhou Instit. Technol. 25 (1996), no. 6, 24–28.
[8] F. Marini and B. Walczak, Particle swarm optimization (PSO), Tutor. Chemomet. Intell. Lab. Syst. 149 (2015),
[9] A. Ruiz-Gazen, S. L. Marie-Sainte and A. Berro, Detecting multivariate outliers using projection pursuit with
particle swarm optimization, Proc. COMPSTAT, 2010 (2010), 89–98.
Volume 13, Issue 2
July 2022
Pages 277-283
  • Receive Date: 11 December 2021
  • Revise Date: 26 January 2022
  • Accept Date: 10 February 2022