Fusion algorithm for locally arranged linear models

Proceedings - International Conference on Pattern Recognition

Volumn 3, Issue , 2006, Pages 1208-1211

  Hoppe, Florian ^a   Sommer, Gerald ^a  

^a UNIVERSITY OF KIEL   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; GRAPH THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

FUSION ALGORITHM; LINEAR MODELS; NEIGHBORHOOD GRAPH;

LINEAR SYSTEMS;

EID: 34147101101     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.590     Document Type: Conference Paper

References (8)

1. J. Bruske and G. Sommer. Dynamic cell structure learns perfectly topology preserving map. Neural Computation, 7(4): 845-865, 1995.
2. S. Chen, C. F. N. Cowan, and P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2:302-309, 1991.
3. K. B. Cho and B. H. Wang. RBF based adaptive fuzzy systems and their applications to system identification and prediction. Fuzzy Sets and Systems, 83:325-339, 1996.
4. F. Hoppe and G. Sommer. Local linear models for system control. In Proceedings of Int. Conf. on Neural Information Processing (ICONIP), pages 171-176, 2005.
5. L. C. Kiong, M. Rajeswari, and M. V. C. Rao. Extrapolation detection and novelty-based node insertion for sequential growing multi-experts network. Neural Network World, 2:151-176, 2003.
6. S. Schaal and C. G. Atkeson. Constructive incremental learning from only local information. Neural Computation, 10(8):2047-2084, 1998.
7. R. Shorten and R. Murray-Smith. Side-effects of normalising basis functions in local model networks. In R. Murray-Smith and T. A. Johansen, editors, Multiple Model Approaches to Modelling and Control, chapter 8, pages 211-228. London: Taylor & Francis, 1997.
8. M. Tagscherer, L. Kindermann, A. Lewandowski, and P. Protzel. Overcome neural limitations for real world applications by providing confidence values for network prediction. In Proceedings of Int. Conf. on Neural Information Processing (ICONIP), pages 520-525, 1999.