My EndNote Database of Neural Network Papers as of September 1, 2003

Note: A 'Yes' at the end of a reference means I definitely have a paper copy.

1. Calvo, R.A., H.D. Navone, and H.A. Ceccatto, Neural network analysis of time series: Applications to climatic data, in Southern Hemisphere paleo- and neoclimates : key sites, methods, data and models, P.P. Smolka and W. Volkheimer, Editors. 2000, Springer: New York. p. 7-16, Yes.

2. Cannon, A.J. and I.G. McKendry, Forecasting all-India summer monsoon rainfall using regional circulation principal components: A comparison between neural network and multiple regression models. International Journal of Climatology, 1999. 19(14): p. 1561-1578, Yes.

3. Cavazos, T., Large-scale circulation anomalies conducive to extreme events and simulation of daily rainfall in northeastern Mexico and southeastern Texas. Journal of Climate, 1999. 12(5): p. 1506-1523, Yes.

4. Cavazos, T., Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans. Journal of Climate, 2000. 13(10): p. 1718-1732, Yes.

5. Cavazos, T., A.C. Comrie, and D.M. Liverman, Intraseasonal variability associated with wet monsoons in southeast Arizona. Journal of Climate, 2002. 15(17): p. 2477-2490, Yes.

6. Crane, R.G. and B.C. Hewitson, Doubled CO2 precipitation changes for the Susquehanna basin: Down-scaling from the GENESIS general circulation model. International Journal of Climatology, 1998. 18: p. 65-76, Yes.

7. Crane, R.G. and B.C. Hewitson, Upscaling of station precipitation records to regional patterns using Self-Organizing Maps (SOMs). Climate Research, accepted, draft.

8. Cross, S.S., R.F. Harrison, and R.L. Kennedy, Introduction to neural networks. The Lancet, 1995. 346: p. 1075-1079, Yes.

9. Demuth, H. and M. Beale, Neural Network Toolbox. 2000, Natick, MA: Mathworks, Inc. 844.

10. Diamantaras, K.I. and S.Y. Kung, Principal component neural networks : theory and applications. Adaptive and learning systems for signal processing, communications, and control. 1996, New York: Wiley. 255.

11. D'Odorico, P., R. Revelli, and L. Ridolfi, On the use of neural networks for dendroclimatic reconstructions. Geophysical Research Letters, 2000. 27(6): p. 791-794, Yes.

12. Efron, B. and R. Tibshirani, An introduction to the bootstrap. 1993, New York, NY: Chapman & Hall.

13. Elsner, J.B. and A.A. Tsonis, Nonlinear Prediction, Chaos, and Noise. Bulletin of the American Meteorological Society, 1992. 73(1): p. 49-60.

14. Gardner, M.W. and S.R. Dorling, Artificial neural networks (the multilayer perceptron) - A review of applications in the atmospheric sciences. Atmospheric Environment, 1998. 32(14-15): p. 2627-2636, Yes.

15. Goodman, P.H., NevProp software, version 4. 1996, University of Nevada: Reno, NV.

16. Hagan, M.T., H.B. Demuth, and M.H. Beale, Neural network design. 1996, Boston: PWS Pub.

17. Hastenrath, S. and L. Greischar, Further Work on the Prediction of Northeast Brazil Rainfall Anomalies. Journal of Climate, 1993. 6: p. 743-758, Yes.

18. Hastenrath, S., L. Greischar, and J. van Heerden, Prediction of the Summer Rainfall over South Africa. Journal of Climate, 1995. 8: p. 1511-1518, Yes.

19. Haykin, S.S., Neural networks : a comprehensive foundation. 2nd ed. 1999, Upper Saddle River, NJ: Prentice Hall. 842.

20. Hewitson, B.C. and R.G. Crane, Precipitation controls in southern Mexico, in Neural Nets: Applications in Geography, B.C. Hewitson and R.G. Crane, Editors. 1994, Kluwer Academic: London. p. 121-143, Yes.

21. Hewitson, B.C. and R.G. Crane, Looks and Uses, in Neural Nets: Applications in Geography, B.C. Hewitson and R.G. Crane, Editors. 1994, Kluwer Academic: London. p. 1-9, No.

22. Hewitson, B.C. and R.G. Crane, eds. Neural Nets: Applications in Geography. 1994, Kluwer Academic: London, No.

23. Hewitson, B.C. and R.G. Crane, Climate downscaling: Techniques and application. Climate Research, 1996. 7: p. 85-95, Yes.

24. Hewitson, B.C. and R.G. Crane, Self-organizing maps: applications to synoptic climatology. Climate Research, 2002. 22(1): p. 13-26.

25. Hewitson, B.C. and R.G. Crane, Gridded area average precipitation via conditional interpolation. Journal of Climate, submitted, No.

26. Hewitson, B.C. and R.G. Crane, Global climate change: Questions of downscaled climate scenarios for impact assessment. Bulletin of the American Meteorological Society, submitted, early draft.

27. Hsieh, W.W., Nonlinear canonical correlation analysis by neural networks. Neural Networks, 2000. 13(10): p. 1095-1105, Yes.

28. Hsieh, W.W., UBC Neural Network Codes for NLPCA/NLCCA. 2001, UBC Climate Prediction Group.

29. Hsieh, W.W., Nonlinear canonical correlation analysis of the tropical Pacific climate variability using a neural network approach. Journal of Climate, 2001. 14(12): p. 2528-2539, Yes.

30. Hsieh, W.W., Nonlinear principal component analysis by neural networks. Tellus, 2001. 53(5): p. 599-615, Yes.

31. Hsieh, W.W. and B.Y. Tang, Applying neural network models to prediction and data analysis in meteorology and oceanography. Bulletin of the American Meteorological Society, 1998. 79(9): p. 1855-1870, Yes.

32. Huth, R., Statistical downscaling in central Europe: evaluation of methods and potential predictors. Climate Research, 1999. 13(2): p. 91-101, Yes.

33. Jain, A.K., J. Mao, and K.M. Mohiuddin, Artificial Neural Networks: A Tutorial. Computer, 1996. 29: p. 31-44, Yes.

34. Karayiannis, N.B. and A.N. Venetsanopoulos, Artificial neural networks : learning algorithms, performance evaluation, and applications. The Kluwer international series in engineering and computer science ; SECS 209. 1993, Boston: Kluwer Academic. xii, 440, No.

35. Karayiannis, N.B. and A.N. Venetsanopoulos, Applications of Neural Networks: A Case Study, in Artificial neural networks : learning algorithms, performance evaluation, and applications, N.B. Karayiannis and A.N. Venetsanopoulos, Editors. 1993, Kluwer Academic: Boston. p. 299-315, Yes.

36. Kohonen, T., The Self Organizing Map. Proceedings of the IEEE, 1990. 78(9): p. 1464-1480, Yes.

37. Kohonen, T., Self-Organizing Maps. Springer Series in Information Sciences. Vol. 30. 1995, Berlin: Springer-Verlag. 362, No.

38. Kohonen, T., et al., SOM PAK: The Self-Organizing Map program package. 1996, Helsinki University of Technology, Laboratory of Computer and Information Science: Espoo, No.

39. Kramer, M.A., Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 1991. 37: p. 233-243, No.

40. Main, J., Seasonality of Circulation in Southern Africa Using the Kohonen Self-Organizing Map. 1997, University of Cape Town. p. 84, No.

41. Malmgren, B.A. and U. Nordlund, Application of artificial neural networks to paleoceanographic data. Palaeogeography Palaeoclimatology Palaeoecology, 1997. 136(1-4): p. 359-373, Yes.

42. Masters, T., Practical Neural Network Recipes in C++. 1993, San Diego, CA: Academic Press. 493, No.

43. MathWorks, Matlab Neural Network Toolbox. 1997, The MathWorks, Inc.: Natick, MA.

44. Monahan, A.H., Nonlinear principal component analysis by neural networks: Theory and application to the Lorenz system. Journal of Climate, 2000. 13(4): p. 821-835, No.

45. Monahan, A.H., Nonlinear principal component analysis: Tropical Indo-Pacific sea surface temperature and sea level pressure. Journal of Climate, 2001. 14(2): p. 219-233, No.

46. Monahan, A.H., L. Pandolfo, and J.C. Fyfe, The preferred structure of variability of the Northern Hemisphere atmospheric circulation. Geophysical Research Letters, 2001. 28(6): p. 1019-1022, Yes.

47. Reed, R.D. and R.J. Marks II, Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. 1999, Cambridge, MA: The MIT Press. 346.

48. Sammon Jr, J.W., A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 1969. C-18(5): p. 401-409.

49. Sarle, W.S., comp.ai.neural-nets FAQ. 1999.

50. Schoof, J.T. and S.C. Pryor, Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks. International Journal of Climatology, 2001. 21(7): p. 773-790, Yes.

51. Snell, S.E., S. Gopal, and R.K. Kaufmann, Spatial interpolation of surface air temperatures using artificial neural networks: Evaluating their use for downscaling GCMs. Journal of Climate, 2000. 13(5): p. 886-895, Yes.

52. Tang, B.Y., G.M. Flato, and G. Holloway, A Study of Arctic Sea-Ice and Sea-Level Pressure Using POP and Neural-Network Methods. Atmosphere-Ocean, 1994. 32(3): p. 507-529, Yes.

53. Tang, B.Y., et al., Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. Journal of Climate, 2000. 13(1): p. 287-293, No.

54. Tangang, F.T., W.W. Hsieh, and B. Tang, Forecasting the equatorial Pacific sea surface temperatures by neural network models. Climate Dynamics, 1997. 13(2): p. 135-147, No.

55. Tangang, F.T., W.W. Hsieh, and B.Y. Tang, Forecasting regional sea surface temperatures in the tropical Pacific by neural network models, with wind stress and sea level pressure as predictors. Journal of Geophysical Research-Oceans, 1998. 103(C4): p. 7511-7522, No.

56. Tangang, F.T., et al., Forecasting ENSO events: A neural network extended EOF approach. Journal of Climate, 1998. 11(1): p. 29-41, No.

57. Tarassenko, L., A Guide to Neural Computing Applications. 1998, New York: John Wiley & Sons, Inc. 139, No.

58. Trigo, R.M. and J.P. Palutikof, Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Climate Research, 1999. 13(1): p. 45-59, Yes.

59. Wienke, D. and P.K. Hopke, Projection of Prim's minimal spanning tree into a Kohonen neural network for identification of airborne particles sources by their multielement trace patterns. Analytica Chimica Acta, 1994. 291: p. 1-18, Yes.

60. Wienke, D., Y. Xie, and P.K. Hopke, Classification of airborne particles by analytical scanning electron microscopy imaging and a modified Kohonen neural network (3MAP). Analytica Chimica Acta, 1995. 310: p. 1-14, Yes.

61. Wilby, R.L. and T.M.L. Wigley, Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography, 1997. 21(4): p. 530-548, Yes.

62. Wilby, R.L., et al., Statistical downscaling of general circulation model output: A comparison of methods. Water Resources Research, 1998. 34(11): p. 2995-3008, Yes.

63. Yuval, Neural network training for prediction of climatological time series, regularized by minimization of the generalized cross- validation function. Monthly Weather Review, 2000. 128(5): p. 1456-1473, Yes.


Web page by David B. Reusch (dbr@essc.psu.edu)
Last modified: Monday, 01-Sep-2003 13:27:20 EDT