An improved evolutionary extreme learning machine based on particle swarm optimization

Neurocomputing

Volumn 116, Issue , 2013, Pages 87-93

  Han, Fei ^a   Yao, Hai Fen ^a   Ling, Qing Hua ^b  

^a JIANGSU UNIVERSITY   (China)

^b JIANGSU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Convergence rate; Extreme learning machine; Generalization performance; Particle swarm optimization

Indexed keywords

CONVERGENCE RATES; EFFICIENCY AND EFFECTIVENESS; EXTREME LEARNING MACHINE; GENERALIZATION PERFORMANCE; HYBRID LEARNING ALGORITHM; NUMBER OF HIDDEN NEURONS; PARTICLE SWARM OPTIMIZATION ALGORITHM; ROOT MEAN SQUARED ERRORS;

FEEDFORWARD NEURAL NETWORKS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO);

LEARNING ALGORITHMS;

ACCURACY; ARTICLE; CONTROLLED STUDY; ERROR; EVOLUTIONARY ALGORITHM; EXTREME LEARNING MACHINE; INSTRUMENT VALIDATION; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PARTICLE SWARM OPTIMIZATION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; QUALITY CONTROL; ROOT MEAN SQUARED ERROR;

EID: 84878507977     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.062     Document Type: Article

References (24)

1. Miche Y., Sorjamaa A., Bas P., Simula O., Jutten C., Lendasse A. OP-ELM: Optimally Pruned Extreme Learning Machine. IEEE Trans. Neural Networks 2010, 21(1):158-162.
2. Huang D.S. Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognition Artif. Intell. 1999, 13(7):1083-1101.
3. Haykin S. Neural Networks: A Comprehensive Foundation 1999, Prentice-Hall, Englewood Cliffs, NJ, USA. second ed.
4. Huang D.S., Du Ji-Xiang A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Networks 2008, 19(12):2099-2115.
5. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme Learning Machine: theory and applications. Neurocomputing 2006, 70(1-3):489-501.
6. Han F., Huang D.S. Improved extreme learning machine for function approximation by encoding a priori information. Neurocomputing 2006, 69(16-18):2369-2373.
7. Huang D.S. A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans. Neural Networks 2004, 15(2):477-491.
8. Huang D.S., Chi Zheru Finding roots of arbitrary high order polynomials based on neural network recursive partitioning method. Sci. China Ser. F. Inf. Sci. 2004, 47(2):232-245.
9. Huang D.S., Ip Horace H.S., Chi Zheru A neural root finder of polynomials based on root moments. Neural Comput. 2004, 16(8):1721-1762.
10. Huang D.S. The local minima free condition of feedforward neural networks for outer-supervised learning. IEEE Trans on Syst. Man Cybern. 1998, 28(3):477-480.
11. Huang D.S., Ip Horace H.S., Law K.C., Chi Zheru Zeroing polynomials using modified constrained neural network approach. IEEE Trans. Neural Networks 2005, 16(3):721-732.
12. Huang D.S., Ip Horace H.S., Chi Zheru, Wong H.S. Dilation method for finding close roots of polynomials based on constrained learning neural networks. Phys. Lett. A 2003, 309(5-6):443-451.
13. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme Learning Machine: a new learning scheme of feedforward neural networks, 2004 International Joint Conference on Neural Networks (IJCNN'2004), Budapest, Hungary, July 25-29, 2004,pp. 985-990.
14. Zhu Q.-Y., Qin A.K., Suganthan P.N., Huang G.-B. Evolutionary extreme learning machine. Pattern Recognition 2005, 38(10):1759-1763.
15. G.P. Zhao, Z.Q. hen, C.Y. Miao, Z.H. Man, On improving the conditioning of extreme learning machine: a linear case, 7th International Conference on Information, Communications and Signal Processing, 2009 (ICICS2009), Maucu, China, December 8-10, 2009, pp. 1-5.
16. Daniel Parrott, Xiaodong Li Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 2006, 10(4):440-458.
17. Langdon W.B., Poli Riccardo Evolving problems to learn about particle swarm optimizers and other search algorithms. IEEE Trans. Evol. Comput. 2007, 11(5):561-578.
18. You Xu, Yang Shu, Evolutionary extreme learning machine-based on particle swarm optimization, International Symposium on Neural Networks 2006 (ISNN2006), LNCS, vol. 3971, 2006, pp. 644-652.
19. R.C. Eberhart, J. Kennedy, A new optimizer using particles swarm theory, in: Proceedings of the Sixth International Symposium on Micro Machine and Human science, Nagoya, Japan, 1995, pp. 39-43.
20. R.C. Eberhart, J. Kennedy, Particle swarm optimization, in: Proceeding of the IEEE International Conference on Neural Network, Perth, Australia, 1995, pp. 1942-1948.
21. Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proceedings of IEEE World Conference on Computation Intelligence, 1998, pp. 69-73.
22. Golub G.H., Van Loan C.F. Matrix Computations 1996, Johns Hopkins University Press.
23. Ghosh R., Verma B. A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning. Int. J. Neural Syst. 2003, 12(1):13-24.
24. Bartlett P.L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theory 1998, 44(2):525-536.