Uli Middelberg: Diplomarbeit Parallele Backpropagation

7 Literaturverzeichnis

Inhaltsverzeichnis für dieses Kapitel, für das gesamte Dokument;


  1. R. BATTITI. First- and Second-Order Methods for Learning: Between Steepest Descent and Newtons's Method. Neural Computation, Vol. 4, pp. 141--166, MIT 1992.
  2. H. BRAUN, P. ZAGORSKI. ENZO--II -- a Powerful Design Tool to Evolve Multilayer Feed Forward Networks. ICEC--94, IEEE 1994.
  3. M. COSNARD, J.C. MIGNOT, H. PAUGAM-MOISY. Implementation of Multilayer Neural Networks on Parallel Architectures. 2nd International Specialist Seminar on The Design and Application of Parallel Digital Processors, pp. 43--47, IEE 1991.
  4. A. GEIST, A. BEGUELIN, J. DONGARRA, W. JIANG, R. MANCHEK, V. SUNDERAM. PVM: Parallel Virtual Machine --- A Users' Guide and Tutorial for Networked Parallel Computing. MIT Press 1994.
  5. I. GLÖCKNER. Monotonic incrementation of backpropagation networks. Proceedings of the International Conference on Artificial Neural Networks (ICANN'93), Amsterdam, p. 498, Springer Verlag 1993.
  6. J. HERTZ, A. KROGH, R. PALMER. Introduction to the Theory of Neural Computation. Addison-Wesley 1991.
  7. J. HOPFIELD. Neural Networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, Vol. 79, pp. 2554--2558, 1982.
  8. K. HORNIK, M. STINCHCOMBE, H. WHITE. Multilayer Feedforward Networks are Universal Approximators. Neural Networks, Vol. 2, pp. 359--366, Pergamon Press 1989.
  9. T. KOHONEN Self-organized formation of topologically correct feature maps. Biological Cybernetics, Vol. 43, pp. 59--69, 1982.
  10. W. MCCULLOCH, W. PITTS. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, Vol. 9, pp. 115--133, 1943.
  11. N. MORGAN, J. BECK, P. KOHN, J. BILMES, E. ALLMAN, J. BEER. The Ring Array Processor: A Multiprocessor Peripheral for Connectionist Applications. Journal of Parallel and Distributed Computing 14(3), pp. 248--259, Academic Press Inc. 1992.
  12. MESSAGE PASSING INTERFACE FORUM. MPI: A Message Passing Interface Standard. University of Tennessee, Knoxville, TN, May 1994.
  13. H. MÜHLENBEIN, K. WOLF. Neural Network Simulation on Parallel Computers. In D.J. Evans, G.R. Joubert, F.J. Peters, (Eds.), Parallel Computing-89, pp. 365--374, North Holland 1990.
  14. U.A. MÜLLER, P. KOHLER, A. GUNZINGER. Neural Net Simulation with MUSIC. IEEE International Conference on Neural Networks, pp. 1737--1741, IEEE 1993.
  15. D. NYCHKA, S. ELLNER, D. MCCAFFREY, A.R. GALLANT. Finding Chaos in Noisy Systems. Journal of the Royal Statistical Society, Series B. 54, pp. 399--426, Royal Statistical Society 1992.
  16. PARSYTEC. Parix V1.3 PowerPC Software Documentation. 1994
  17. H.-O. PEITGEN, H. JÜRGENS, D. SAUPE. Bausteine des Chaos --- Fraktale. Klett-Cotta/Springer-Verlag 1992.
  18. R. ROJAS. Theorie der neuronalen Netze --- Eine systematische Einführung. Springer Verlag 1993.
  19. F. ROSENBLATT. The perceptron: a probabilistic model for information storage and organisation in the brain. Psychological Review, Vol 65, pp. 386--408, 1958.
  20. D.E. RUMELHART, G.E. HINTON, R.J. WILLIAMS. Learning internal representations by error propagation. In D.E. Rumelhart and J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, pp. 318--362, MIT 1987.
  21. G. SCHWARZ. Estimating the Dimension of a Model. The Annals of Statistics, Vol. 6, No. 2, pp. 461--464, 1978.
  22. F. M. THIESING, U. MIDDELBERG, O. VORNBERGER. A Neural Network Approach for Predicting the Sale of Articles in Supermarkets. Proceedings of the 3rd European Congress on Intelligent Techniques and Soft Computing (EUFIT '95), Vol. 1, pp. 315--320, Aachen 1995.
  23. F. M. THIESING, U. MIDDELBERG, O. VORNBERGER. Parallel Back-Propagation for Sales Prediction on Transputer Systems. In B.M. Cook, M.R. Jane, P. Nixon, P.M. Welch, (Eds.), Transputer Applications and Systems '95, Proceedings of the 1995 World Transputer Congress, pp. 318--331, IOS Press 1995.
  24. V.R. VEMURI, R.D. ROGERS. Artificial Neural Networks --- Forecasting Time Series. IEEE Computer Society Press 5120-05, 1994.
  25. H. WHITE. Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings. Neural Networks, Vol. 3, pp. 535--549, Pergamon Press 1990.
  26. B. WIDROW, D.E. RUMELHART, M.A. LEHR. Neural Networks: Applications in Industry, Business and Science. CACM, Vol. 37, No. 3, pp. 93--105, 1994.
  27. M. WYNNE-JONES. Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks. In J.E. Moody, S.J. Hanson and R.P. Lippmann (Eds.), Neural Information Processing Systems 4, pp. 1072--1079, Morgan Kaufmann Publishers 1992.
  28. H. YOON, J.H. NANG, S.R. MAENG. A distributed backpropagation algorithm of neural networks on distributed-memory multiprocessors. Proceedings of the 3rd symposium on the Frontiers of Massively Parallel Computation, pp. 358--363, IEEE 1990.


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Uli Middelberg: Mon Sep 11 21:41:47 MET DST 1995