A Novel Intelligent Modeling Method for Wood Drying Process

Abstract:

This paper investigates the development and intelligent modeling problem for a wood drying kiln process via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithms of Particle Swarm Optimization (PSO)-SVM and Genetic Algorithm (GA)-SVM were proposed for wood drying process with strong coupling and nonlinear characteristics. The simulation results showed that both of these two kinds of swarm intelligence optimization algorithm could get the appropriate parameters of SVM effectively, and by contrast, PSO showed a better learning ability and generalization in wood drying process modeling, and could establish predictive model with better accessibility.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 647-651
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.647
• Online since: October 2011
• Authors: Dong Yan Zhang, Chun Yan Zhang, Liang Kuan Zhu, Zhi Duo Diao
• Keywords: GA, Modeling, Particle Swarm Optimization Algorithm (PSO), Support Vector Machine (SVM), Swarm Intelligence, Wood Drying

References

[1] S. Pang, Relative. Importance of vapor diffusion and convective flow in modelling of softwood drying. Drying Technique, Vol. 16. 1-2, (1998), p.271–281.

DOI: 10.1080/07373939808917403

[2] F. More-Chevalier and J.M. Frayret, A fuzzy logic application to kiln dryer regulation. Proceeding of 5th International IUFRO Wood Drying Conference, Quebec City, Canada, August 13–17, (1996), p.221–229.

[3] G.C.K. Yan, C.W. de Silva and X.G. Wang, Experimental modeling and intelligent control of a wood drying kiln. Adaptive Control and Signal Processing, Vol. 15. 8, (2001), p.787–814.

DOI: 10.1002/acs.656

[4] B.G. Zhang and T.L. Liu, To explore new development path of wood drying technology in China, Drying Technology and Equipment, (2006), Vol. 4, pp.7-13.

[5] P. Bai, X.B. Zhang, and B. Zhang, Support Vector Machines theory and engineering application, Xi'an University of Electronic Science and Technology Press, (2008).

[6] L.W. Xiong and B.G. Xu, SVR parameter optimization selection based on PSO method, Journal of System Simulation, Vol. 18, (2006), pp.2442-2445.

[7] Z. Ji, Algorithm and Application of PSO. Science Press, (2009).

[8] W.F. Yin and D.Y. Zhang, Application of PSO optimized SVM in Wood Drying Modeling, Mechanical and electronic product development and innovation, Vol. 23, (2010), pp.97-99.

[9] X.P. Wang and L.M. Cao. Genetic Algorithm – Theory , Application and Software Implementation. Xi'an Jiaotong University Press, (2002).