On-Line Modeling of Dynamic Nonlinear System Based on Bayesian Inferring Method Combined with Evolutionary Algorithms

Yi Jian Liu; Li Ming Di; Yan Jun Fang

doi:10.4028/www.scientific.net/AMR.532-533.1640

Paper Titles

Image Mosaic Method Based on SUSAN Algorithm and Log-Polar Transformation
p.1621

An Extended Simplex Algorithm for Linear Programming
p.1626

An Efficient Algorithm for Improved Shape Context Feature's Cost Matrix Computation
p.1631

Approach for Solving Nonlinear Equation Group Based on Adaptive Genetic Algorithm
p.1636

On-Line Modeling of Dynamic Nonlinear System Based on Bayesian Inferring Method Combined with Evolutionary Algorithms
p.1640

The Research of PApriori Algrithm Based on OLAP
p.1645

Improved Algorithm for Discretization of Decision Table
p.1649

Study on Coarse-Grained Parallel Genetic Algorithm
p.1654

Dual Aircrafts Path Planning for Forest-Fire Rescue by Artificial Immune Algorithm
p.1659

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533On-Line Modeling of Dynamic Nonlinear System Based...

On-Line Modeling of Dynamic Nonlinear System Based on Bayesian Inferring Method Combined with Evolutionary Algorithms

Abstract:

A novel modeling method based on Bayesian inferring for dynamic nonlinear system is proposed in this article. The Bayesian inferring model structure and its training algorithm combined with evolutionary algorithms are first described in which the matrix threshold D parameters are optimized by evolutionary algorithms and the structure of the Bayesian inferring model is updated by the system running data. Then some typical dynamic systems are used for validating the modeling effectiveness of the Bayesian inferring method. And the simulation results are presented and some conclusion on the Bayesian inferring modeling method is described in details at last.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1640-1644

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1640

Citation:

Cite this paper

Online since:

June 2012

Authors:

Yi Jian Liu, Li Ming Di, Yan Jun Fang

Keywords:

Bayesian Inferring, Evolutionary Algorithm (EA), Modeling, Nonlinear System

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] M. özger: Expert Systems with Applications, Vol. 38, (2011) No. 5, p.6269.

Google Scholar

[2] C.W. Tao and J.S. Taur: IEEE Transactions on Fuzzy Systems, Vol. 13 (2005) No. 1, p.30.

Google Scholar

[3] S.A. Billings, F.L. Wei and M.A. Balikhin: Neural Networks, Vol. 20 (2007) No. 10, p.1081.

Google Scholar

[4] S.P. Tian and G.Q. Ding: Journal of Mechanical Engineering, Vol. 17 (2004) No. 2, p.306.

Google Scholar

[5] J.N. Choi, S.K. Oh and W. Pedrycz: Fuzzy Sets and Systems, Vol. 159 (2008) No. 21, p.2873.

Google Scholar

[6] S.Y. Li, Q. Chen and G.B. Huang: Neurocomputing, Vol. 69 (2006) No. 4-6, p.523.

Google Scholar

[7] H.W. Ye, R.N. Nicolai and L. Reh: Chemical Engineering and Process, Vol. 38 (1998), p.439.

Google Scholar

[8] Y.J. Liu and C. Peng: International Conference on Natural Computing (Tianjing, China, August 14-16, 2009), Vol. 1, p.353.

Google Scholar

[9] C.C. Lin: Computer & Industrial Engineering, Vol. 50 (2006) No. 1-2, p.175.

Google Scholar

[10] Y. Jiang and C.M. Liu: Applied Mathematics and Computation, Vol. 217 (2010) No. 7, p.3207.

Google Scholar

[11] R. Majhia and G. Pandab: Expert Systems with Application, Vol. 36 (2009) No. 6, p.10097.

Google Scholar

[12] Y.J. Fang and Y.J. Liu: IEEE International Conference on Automation and Logistics (Jinan, China, September 1-3, 2008), Vol. 1, p.238.

Google Scholar