Comparison of EM algorithm and particle swarm optimisation for local model network training

2010 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2010

Volumn , Issue , 2010, Pages 267-272

  Hametner, Christoph ^a   Jakubek, Stefan ^a  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Expectation Maximisation; Local model network; Particle swarm optimisation

Indexed keywords

EM ALGORITHMS; EXPECTATION-MAXIMISATION; ILLUSTRATIVE EXAMPLES; LOCAL MODEL NETWORKS; NONLINEAR STATICS; OPTIMISATIONS; PARTICLE SWARM OPTIMISATION; PERFORMANCE CRITERION; PSO ALGORITHMS; REAL MEASUREMENTS; SEARCH SPACES;

CYBERNETICS; DYNAMICAL SYSTEMS; EVOLUTIONARY ALGORITHMS; INTELLIGENT SYSTEMS; INTERNAL COMBUSTION ENGINES; OPTIMIZATION;

TREES (MATHEMATICS);

EID: 77956075942     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCIS.2010.5518547     Document Type: Conference Paper

References (16)

1. O. Nelles. Nonlinear System Identification. Springer Verlag, 1st edition, 2002.
2. S. Jakubek and N. Keuth. A Local Neuro-Fuzzy Network for High-Dimensional Models and Optimisation. Engineering Applications of Artificial Intelligence, 19:705-717, September 2006.
3. S. Jakubek, N. Keuth. Improved Neuro-Fuzzy Models for Design Processes and Simulation in Automotive Applications. at-Automatisierungstechnik, 09/2005 2005.
4. S. Jakubek, N. Keuth. A New Training Algorithm for Neuro-Fuzzy Networks. In Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ANNIIP 2005), September 2005.
5. C. Hametner and S. Jakubek. Neuro-fuzzy modelling using a logistic discriminant tree. American Control Conference, 2007. ACC '07, pages 864-869, July 2007.
6. S. Jakubek and C. Hametner. Identification of Neurofuzzy Models Using GTLS Parameter Estimation. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(5):1121-1133, Oct. 2009.
7. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA, 1984.
8. O. Nelles. LOLIMOT - Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme. Automatisierungstechnik (at), pages 163-174, April 1997.
9. L. Breiman. Hinging Hyperplanes for Regression, Classification, and Function Approximation. In IEEE Transactions on Information Theory, volume 39. IEEE, 1993.
10. P. Pucar and M. Millnert. Smooth hinging hyperplanes - an alternative to neural networks. In Proceedings of the 3rd ECC, 1995.
11. S. Ernst. Hinging Hyperplane Trees for Approximation and Identification. In Proceedings of the 37th IEEE Conference on Decicion & Control 1998, volume 2, pages 1266 - 1271. IEEE, 1998.
12. R. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In A new optimizer using particle swarm theory, pages 39-43, 1995.
13. Ioan Cristian Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6):317 - 325, 2003.
14. Sergios Theodoridis and Konstantinos Koutroumbas. Pattern Recognition. Academic Press, San Diego, CA, USA, 1999.
15. Christopher M. Bishop. Neural networks for pattern recognition. Oxford University Press, USA, 1995.
16. J. Abonyi, R. Babuska, and F. Szeifert. Modified Gath-Geva Fuzzy Clustering for Identification of Takagi-Sugeno Fuzzy Models. In IEEE Transactions on Systems, Man and Cybernetics, volume 32, pages 612-621. IEEE, 2002.