Gamma-filter self-organizing neural networks for time series analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6731 LNCS, Issue , 2011, Pages 151-159

  Estévez, Pablo A ^a   Hernández, Rodrigo ^a  

^a UNIVERSITY OF CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DESCRIPTORS; GAMMA FILTERS; GROWING NEURAL GAS; MACKEY-GLASS TIME SERIES; NEURAL GAS; PERFORMANCE MEASURE; QUANTIZATION ERRORS; SELF-ORGANIZING NEURAL NETWORK; SHORT TERM MEMORY; SIMULATION RESULT; SINGLE STAGE; TEMPORAL SEQUENCES;

CONFORMAL MAPPING; SELF ORGANIZING MAPS; TIME SERIES; BANDPASS FILTERS; NEURAL NETWORKS;

TIME SERIES ANALYSIS;

EID: 79959294297     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21566-7_15     Document Type: Conference Paper

References (17)

1. Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995).
2. Strickert, M., Hammer, B.: Merge SOM for Temporal Data. Neurocomputing 64, 39-72 (2005).
3. Voegtlin, T.: Recursive Self-Organizing Maps. Neural Networks 15, 979-991 (2002).
4. Hammer, B., Micheli, A., Neubauer, N., Sperduti, A., Strickert, M.: Self Organizing Maps for Time Series. In: Proceedings of the Workshop on Self-Organizing Maps (WSOM), Paris, pp. 115-122 (2005).
5. Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive Self-Organizing Network Models. Neural Networks 17, 1061-1085 (2004). (Pubitemid 39539528)
6. Chappell, G.J., Taylor, J.G.: The Temporal Koḧonen Map. Neural Networks 6, 441-445 (1993).
7. Tiñeo, P., Farkas, I., Van Mourik, J.: Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Computation 18, 2529-2567 (2006). (Pubitemid 44511774)
8. Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: "Neuralgas" Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Transactions on Neural Networks 4, 558-569 (1993).
9. Fritzke, B.: A Growing Neural Gas Learns Topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) NIPS, pp. 625-632. MIT Press, Cambridge (1995).
10. Strickert, M., Hammer, B.: Neural Gas for Sequences. In: Yamakawa, T. (ed.) Proceedings of the Workshop on Self-Organizing Networks (WSOM), Kyushu, Japan, pp. 53-58 (2003).
11. Andreakis, A., Hoyningen-Huene, N.V., Beetz, M.: Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas. In: Pŕncipe, J.C., Miikkulainen, R. (eds.) WSOM 2009. LNCS, vol. 5629, pp. 10-18. Springer, Heidelberg (2009).
12. De Vries, B., Principe, J.C.: The Gamma Model-A New Neural Model for Temporal Processing. Neural Networks 5, 565-576 (1992). (Pubitemid 23566699)
13. Est́evez, P.A., Herńandez, R.: Gamma SOMfor Temporal Sequence Processing. In: Pŕncipe, J.C., Miikkulainen, R. (eds.) WSOM 2009. LNCS, vol. 5629, pp. 63-71. Springer, Heidelberg (2009).
14. Estevez, P.A., Hernandez, R., Perez, C.A., Held, C.M.: Gamma-filter Self-organizing Neural Networks for Unsupervised Sequence Processing. Electronics Letters (in press, 2011).
15. Principe, J.C., Giuliano, N.R., Lefebvre, W.C.: Neural and Adaptive Systems. John Wiley & Sons, Inc., New York (1999).
16. Principe, J.C., Kuo, J.M., Celebi, S.: An Analysis of the Gamma Memory in Dynamic Neural Networks. IEEE Trans. on Neural Networks 5, 331-337 (1994).
17. Principe, J.C., De Vries, B., De Oliveira, P.G.: The Gamma Filter -A New Class of Adaptive IIR Filters with Restricted Feedback. IEEE Transactions on Signal Processing 41, 649-656 (1993)