Discrete particle swarm optimization for automating the design of multivariate autoregressive neural networks

WSEAS Transactions on Information Science and Applications

Volumn 2, Issue 11, 2005, Pages 1807-1815

  Kalos, Alex ^a  

^a DOW CHEMICAL COMPANY   (United States)

Author keywords

Discrete Particle Swarm Optimization; Neural network structure optimization

Indexed keywords

FORECASTING; MATHEMATICAL MODELS; OPTIMIZATION; TIME SERIES ANALYSIS;

DISCRETE PARTICLE SWARM OPTIMIZATION; NEURAL NETWORK STRUCTURE OPTIMIZATION;

NEURAL NETWORKS;

EID: 24344452610     PISSN: 17900832     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1997.
2. G. Leng, G. Prasad, and T.M. McGinnity, An online algorithm for creating self-organizing neural networks, Neural Networks, 17, 2004, pp.487-493.
3. B. Hammer, A. Micheli, A. Sperduti, and M. Strickert, Recursive self-organizing network models Neural Networks, 17, 2004, pp. 1061-1085.
4. S. Haykin, Neural Networks: A Comprehensive Foundation, Second Edition, Prentice Hall, New Jersey, 1999
5. C-T. Chen and W-D. Chang, A Feedforward Neural Network with Function Shape Auto-tuning, Neural Networks, Vol. 9, No. 4, 1996, pp.627-641.
6. S-K. Oh and W. Pedrycz, A new approach to self-organizing multi-layer fuzzy polynomial neural networks based on genetic optimization, Advanced engineering Infomatics, 18, 2004, pp. 29-39.
7. I. Pitas, C. Kotropoulos, N. Nikolaidis, and A. Bors, Robust and Adaptive Techniques in Self-Organizing Neural Networks, Nonlinear Analysis, Theory & Applications, Vol. 30, No. 7, 1997, pp 4517-4528.
8. A. Ghodi and D. Schuurmans, Automatic basis selection techniques for RBF networks, Neural Networks, 16, 2003, pp. 809-816.
9. D. B. Fogel, L. J. Fogel, and V. W. Porto, Evolving Neural Networks, Biol. Cybern., 66, 1990, pp. 487-493.
10. A. N. Kalos, Automated Heuristic Growing of Neural Networks for Nonlinear Time Series Models, IEEE International Joint Conference On Neural Networks, Montreal, Canada, 2005, in press.
11. N. Garcia-Pedrajas, C. Hervas-Martinez, and J. Munoz-Perz, Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks), Neural Networks, 15, 2004, pp. 1259-1278.
12. Mathematica 5.0 by Wolfram Research, http://www.wolfram.com. The Neural Networks package is an add-on component to the standard Mathematica software and must be purchased separately.
13. J. Kennedy and R. C. Eberhart, Particle Swarm Optimization, IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, Piscataway, NJ, 1995.
14. J. Kennedy, R.C. Eberhart, and Y. Shi, Swarm Intelligence, San Francisco: Morgan Kaufmann, CA, 2001.
15. S. R. Katare, A. N. Kalos, D. H. West, A Hybrid Swarm Optimizer for Efficient Parameter Estimation, Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, Portland, Oregon, June 19-23, 2004, pp. 309-315.
16. Kennedy, J. and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, International Conference on Systems, Man, and Cybernetics, 1997.
17. M. Kearns, A bound on error of cross validation using the approximation and estimation rates, with consequences for the training-test split, Advances in Neural Information Processing Systems, Vol. 8, Cambridge, MA, 1996, pp. 183-189.
18. D.W. Marquardt, Journal of the Society for Industrial and Applied Mathematics, vol. 11, 1963, pp. 431-441.
19. R. Fletcher, Practical Methods of Optimization, John Wiley & Sons, Chippenham, Great Britain, 1987.
20. P. J. Werbos, Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, Cambridge, MA, 1974.
21. J. Sjoberg and L. Ljung, Overtraining, Regularization, and Searching for Minimum with Application to Neural Nets, Int. J. Control, vol. 62, no. 6, 1995, pp. 1391-1407.
22. J. Sjoberg and M. Viberg, Separable Non-linear Least-squares minimization - Possible Improvements for Neural Net Fitting, IEEE Workshop in Neural Networks for Signal Processing, Amelia Island Plantation, Florida, Sep. 24-26, 1997, pp. 345-354.
23. Electric Reliability Council of Texas (ERCOT), http://www.ercot.com/.
24. New York Mercantile Exchange (NYMEX), http://www.nymex.com/.
25. G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis Forecasting and Control, Third Edition., Pearson Education, Inc, 1994.