178 
[Ponte 92] Ponte, R.M.: The sea level response of a stratified ocean to barometric pressure 
forcing. Journal of Physical Oceanography, Vol. 22, pp. 109-113, American Meteoro 
logical Society, 1992. 
[Ponte 93] Ponte, R.M.: Variability in a homogeneous global ocean forced by barometric 
pressure. Dynamics of Atmospheres and Oceans, Vol. 18, pp. 209-234, Elsevier 
Science Publishers B.V., Amsterdam, 1993. 
[Ponte 94] Ponte, R.M.: Understanding the relation between wind- and pressure-driven sea 
level variability. Journal of Geophysical Research, Vol. 99, Nr. C4, pp.8033-8039, 
Amercan Geophysical Union, 1994. 
[Preisendorfer 88] Preisendorfer, R.W.: Principal Component Analysis in Meteorology and 
Oceanography. Elsevier, Amsterdam, 1988. 
[Press et al. 91] Press, W.H.; Flannery, B.P.; Teukolsky, S.A.; Vetterling, W.T.: Numerical 
Recipes in C. The Art of Scientific Computing, Cambridge University Press, 1991. 
[Ramacher 92] Ramacher, U.: Synapse - A Neurocomputer That Synthesizes Neural Algo 
rithms on a Parallel Systolic Engine. Journal of Parallel and Distributed Computing 
14, pp. 306-318, 1992. 
[Ramacher et al. 94] Ramacher, U.; Raab, W.; Anlauf, J.; Hachmann, U.; Weßeling, M.: 
SYNAPSE-1 — ein General-Purpose Neurocomputer. Siemens AG Proprietary Infor 
mation, 1994. 
[Rauschelbach 24] Rauschelbach, H.: Harmonische Analyse der Gezeiten des Meeres. Aus 
dem Archiv der Deutschen Seewarte, XLII. Jg., Nr.l, Hamburg, 1924. 
[Refenes et al. 93] Refenes, A.N.; Azema-Barac, M.; Chen, L.; Karoussos, S.A.: Currency 
Exchange Rate Prediction and Neural Network Design Strategies. Neural Comput. & 
Applic., Springer-Verlag London Limited, 1993. 
[Rehkugler et al. 92] Rehkugler, H.; Poddig, T.: Anwendungsperspektiven und Anwendungs 
probleme von Künstlichen Neuronalen Netzwerken. Information Management 2/92. 
[Ritter et al. 92] Ritter, H.; Martinetz, T.; Schulten, K.: Neuronale Netze. Eine Einführung in 
die Neuroinformatik selbstorganisierender Netzwerke. Addison-Wesley Publishing 
Company, 1992. 
[Rodrigues et al. 92] Rodrigues, A.J.; Young, P.C.; Tych, W.: Application of Neural Net 
works to Advanced Time Series Model Identification. IDG VSP, 1992. 
[Rumelhart et al. 86a] Rumelhart, D.E.; Hinton, G.E.; Williams, R.J.: Learning representa 
tions by back-propagating errors. Nature Vol. 232, 1986.