Time dependent neural network models for detecting changes of state in Earth and planetary processes

Proceedings of the International Joint Conference on Neural Networks

Volumn 3, Issue , 2005, Pages 1710-1715

  Valdés, Julio J ^a   Bonham Carter, Graeme ^b  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^b GEOLOGICAL SURVEY OF CANADA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; EARTH (PLANET); FUZZY CONTROL; MULTIVARIABLE SYSTEMS; PLANETARY SURFACE ANALYSIS; TIME SERIES ANALYSIS;

MULTIVARIATE TIME SERIES; NEURO-FUZZY NETWORKS; PREDICTION CAPABILITIES;

NEURAL NETWORKS;

EID: 33750106681     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1556137     Document Type: Conference Paper

References (19)

1. J. Amoch and M. Huškova, Estimators of Changes. Asymptotics, Nonparametrics, and Time Series (S. Gosh ed). Marcel Dekker Inc, New York, 1999, pp. 533-578.
2. LI. Belanche, Heterogeneous neural networks: Theory and applications. PhD Thesis, Department of Languages and Informatic Systems, Polytechnic University of Catalonia, Barcelona, Spain, July 2000.
3. G. Box and G. Jenkins, Time Series Analysis, Forecasting and Control, Prentice Hall, 1976.
4. J.L. Chandon and S. Pinson,Analyse typologique. Thorie et applications Masson, Paris, 1981.
5. W. Dansgaard, S.J. Johnsen, J. Moller, and C.C. Langway, Jr. One thousand centuries of climatic record from Camp Century on the Greenland ice sheet. Science vol. 166, 1969 pp. 377-381.
6. L.B. Hansen, and C.C. Langway, Jr. Deep core drilling in ice and core analysis at Camp Century, Greenland, 1961-1966. CRREL Spec. Report 126, no. 5, 1966, pp. 207-208.
7. S.J. Johnsen, W. Dansgaard, H.B. Clausen, and C.C. Langway, Jr. Climatic oscillations 1200-2000 A.D. Nature vol. 227, 1970, pp. 482-483.
8. S.J. Johnsen, W. Dansgaard, H.B. Clausen, and C.C. Langway, Jr. Oxygen isotope profiles through the Antarctic and Greenland ice sheets. Nature vol. 235, 1972 pp. 429-434.
9. T. Masters, Neural, Novel & Hybrid Algorithms for Time Series Prediction, John Wiley & Sons, 1995.
10. V. Petridis and A. Kehagias, Predictive Neural Networks. Applications to Time Series. Kluver 1998.
11. A. Pole, M. West, J. Harrison, Applied Bayesian Forecasting and Time Series Analysis., CRC Press, 1994.
12. G. C., Reinsel, Elements of Multivariate Tine Series Analysis., Springer Verlag, 1993.
13. J. J. Valdés, and R., García, A model for heterogeneous neurons and its use in configuring neural networks for classification problems. Proceedings of the Int. Work-Conf. on Artificial and Natural Neural Networks, IWANN'97. Biological and Artificial Computation: From Neuroscience to Technology. Lecture Notes in Computer Science 1240, Springer-Verlag, Lanzarote, Spain, 1997, pp 237-246.
14. J. J. Valdés, LI. Belanche, R. Alquézar, Fuzzy Heterogeneous Neurons for Imprecise Classification Problems. International Journal ofIntelligent Systems vol. 15, no. 3, 2000, pp.
15. J. J. Valdés, Time Series Models Discovery with Similarity-Based Neuro-Fuzzy Networks and Evolutionary Algorithms. Proc. IEEE World Congress on Computational Intelligence WCCI'2002. Hawaii, USA, 2002.
16. J. J. Valdés and G. Mateescu, Time Series Models Discovery with Similarity-Based Neuro-Fuzzy Networks and Genetic Algorithms: A Parallel Implementation. Third. Int. Conf. on Rough Sets and Current Trends in Computintg RSCTC 2002. Malvem, PA, USA. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence Series) LNCS 2475, Springer-Verlag, 2002, pp. 279-288.
17. J. J. Valdés, Similarity-based heterogeneous neurons in the context of general observational models Neural Network World vol. 5, 2002, pp. 499-508.
18. J. J. Valdés and A.J. Barton, Multivariate Time Series Model Discovery with Similarity-Based Neuro-Fuzzy Networks and Genetic Algorithms. Proceedings of the IEEE, INNS, IJCNN 2003 International Joint Conference on Neural Networks, Portland, Oregon, July 20-24, 2003, pp. 1945-1950.
19. J. J. Valdés, Detecting Changes of State in Heterogeneous Dynamic Processes with Time-dependent Models: A Soft-Computing Approach. Proceedings of the IASTED International Joint Conference on Artificial Intelligence and Soft Computing, ASC'2004, Marbella, Spain. ACTA Press, Anaheim, USA, 2004, pp. 155-160.