Temperature Identification of Electrical Heating Furnace Based on Elman Network Combined with Improved PSO

Shan Ding; Yixin Yin; Wei Huang; Jie Dong; Xue Ming Ma

doi:10.4028/www.scientific.net/AMM.20-23.82

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 20-23Temperature Identification of Electrical Heating...

Temperature Identification of Electrical Heating Furnace Based on Elman Network Combined with Improved PSO

Abstract:

A temperature model of the electrical heating furnace which is commonly used in industry is built by means of Elman neural network combined with an improved particle swarm optimization (IPSO). The input is duty cycle, and IPSO is used to optimize the weights and threshold values of Elman neural network to improve convergence capability and generalization performance of Elman network. Results show that the method performs better than ordinary Elman network in convergence speed and accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 20-23)

Pages:

82-87

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.20-23.82

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Online since:

January 2010

Authors:

Shan Ding, Yixin Yin, Wei Huang, Jie Dong, Xue Ming Ma

Keywords:

Electrical Heating Furnace, Elman Neural Network (ENN), Nonlinear Time-Varying Evolution, Particle Swarm Optimization Algorithm (PSO)

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References

[1] L. Wang, B. Liu: Particle Swarm Optimization and Scheduling Algorithms (Tsinghua University Press, China 2008).

Google Scholar

[2] J. Kennedy, R.C. Eberhart: Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks Vol. 4 (1995), p.1942-(1948).

Google Scholar

[3] J.C. Zeng, J. Jie, Z.H. Cui: Particle Swarm Optimization (Science Press, China 2004).

Google Scholar

[4] Y. Shi, R.C. Eberhart: A modified particle swarm optimizer. Proceedings of the IEEE Congress on Evolutionary Computation (1998), pp.69-73.

DOI: 10.1109/icec.1998.699146

Google Scholar

[5] R.C. Eberhart, Y. Shi: Tracking and optimizing dynamic systems with particle swarm. Proceedings of the IEEE International Congress on Evolutionary Computation Vol. 1 (2001), pp.94-100.

DOI: 10.1109/cec.2001.934376

Google Scholar

[6] C.N. Ko, Y.P. Chang and C.J. Wu: An orthogonal-array-based particle swarm optimizer with nonlinear time-varying evolution. Applied Mathematics and Computation Vol. 191 (2007), pp.272-279.

DOI: 10.1016/j.amc.2007.02.096

Google Scholar

[7] C.M. Lee, C.N. Ko: sumitted to Neuralcomputing (2009).

Google Scholar

[8] A. Ratnaweera, S.K. Halgamuge and H.C. Watson: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation Vol. 8 (2004), pp.240-255.

DOI: 10.1109/tevc.2004.826071

Google Scholar