References

Aka69

H. Akaike. Annals of the institute of statistical mathematics, 21, chapter Fitting autoregressive models for prediction. 1969.

Ama89

Shun-Ichi Amari. Characteristics of Encoded Associative Memory. Springer Verlag 1989, 1989.

BD95

Michael R. Berthold and Jay Diamond. Boosting the performance of rbf networks with dynamic decay adjustment. In G. Tesauro, D. S. Touretzky, and T.K. Leen, editors, Advances in Neural Information Processing Systems, volume 7, 1995.

BH93

D. Stork B. Hassibi. Second order derivatives for network pruning: Optimal Brain Surgeon. In T. J. Sejnowski G. E. Hinton and D. S. Touretzky, editors, Advances in Neural Information Processing Systems (NIPS) 5, pages 164--171, San Mateo, 1993. Morgan Kaufmann Publishers Inc.

Bie94

J. Biedermann. Anwendungen Neuronaler Netze beim VLSI-CAD. Diplomarbeit, Institut für Numerische und Angewandte Mathematik, Georg-August-Universität Göttingen, 1994.

CG87a

G. A. Carpenter and S. Grossberg. A Massively Parallel Architecture for a Selforganizing Neural Pattern Recognition Machine. Computer Vision, Graphics and Image Processing, 37:54--115, 1987.

CG87b

G. A. Carpenter and S. Grossberg. Stable self-organization of pattern recognition codes for analog input patterns. Applied Optics, 26:4919--4930, 1987.

CG91

G. A. Carpenter and S. Grossberg. ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network. Neural Networks, 4:543--564, 1991.

D.E93

D.Elliott. A better activation function for artificial neural networks. ISR Technical Report TR 93-8, University of Maryland, 1993.

DH73

R. Duda and P. Hart. Pattern Classification and Scene Analysis. Wiley & Sons, Inc, 1973.

Elm90

J. L. Elman. Finding structure in time. Cognitive Science, 14:179--211, 1990.

Fah88

Scott E. Fahlman. Faster-learning variations on back-propagation: An empirical study. In T. J. Sejnowski G. E. Hinton and D. S. Touretzky, editors, 1988 Connectionist Models Summer School, San Mateo, CA, 1988. Morgan Kaufmann.

Fah91

S. E. Fahlman. The recurrent cascade-correlation architecture. Technical Report CMU-CS-91-100, School of Computer Science, Carnegie Mellon University, 1991.

FL91

S. E. Fahlman and C. Lebiere. The cascade-correlation learning architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University, August 1991.

GFL86

N.H. Goddard, M.A. Fanty, and K. Lynne. The Rochester Connectionist Simulator. Technical Report 189, Computer Science Department, Univ. of Rochester, June 1986.

GHW79

G. H. Golub, M. Heath, and G. Wahba. Technometrics, 21, chapter Generalized cross-calidation as a method for choosing ridge parameter. 1979.

GLML89

N. Goddard, K.J. Lynne, T. Mintz, and L.Bukys. The Rochester Connectionist Simulator. Technical Report 233 (revised), Univ. of Rochester, NY, oct 1989.

God87

N. Goddard. The Rochester Connectionist Simulator: User Manual. Univ. of Rochester, NY, 1987.

Har83

S. Harrington. Computer Graphics - A Programming Approach. McGraw-Hill, 1983.

Her92

K.-U. Herrmann. ART -- Adaptive Resonance Theory -- Architekturen, Implementierung und Anwendung. Diplomarbeit 929, IPVR, Universität Stuttgart, 1992.

HF91

M. Hoefeld and S. E. Fahlman. Learning with limited numerical precision using the cascade-correlation algorithm. Technical Report CMU-CS-91-130, School of Computer Science, Carnegie Mellon University, 1991.

Hil85

W.D. Hillis. The Connection Machine. MIT Press, 1985.

HS86a

W.D. Hillis and G.L. Steele. Data parallel algorithms. ACM, 29(12):1170--1183, 1986.

HS86b

W.D. Hillis and G.L. Steele. Massively parallel computers: The Connection Machine and NONVON. Science, 231(4741):975--978, 1986.

Hüb92

R. Hübner. 3d--Visualiserung der Topologie und der Aktivität neuronaler Netze. Diplomarbeit 846, IPVR, Universität Stuttgart, 1992.

Hud92

M.J. Hudak. Rce classifiers: Theory and practice. Cybernetics and Systems, pages 483--515, 1992.

JGHL80

G. G. Judge, W. E. Griffiths, R. C. Hill, and T. Lee. The theory and practice of econometrics. John Wiley & sons, 1980.

Jor86a

M. I. Jordan. Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the Eighth Annual Conference of the Cognitive Science Society, pages 531--546, Hillsdale NJ, 1986. Erlbaum.

Jor86b

M. I. Jordan. Serial order: A parallel distributed processing approach. Technical Report Nr. 8604, Institute for Cognitive Science, University of California, San Diego, La Jolla, California, 1986.

JS91

R. Dow J. Sietsma. Creating Artificial Neural Networks That Generalize. Neural Networks, 4(1):67--79, 1991.

KKLT92

T. Kohonen, J. Kangas, J. Laaksoonen, and K. Torkkola. Lvq_pak learning vector quantization program package. Technical report, Laboratory of Computer and Information Science Rakentajanaukio 2 C, 1991 - 1992.

KL90

J. Kindermann and A. Linden. Inversion of neural networks by gradient descent. Parallel Computing, 14:277--286, 1990.

Koh88

T Kohonen. Self-Organization and Associative Memory. Springer-Verlag, 1988.

Koh89

Teuvo Kohonen. Self-Organization and Associative Memory, Third Edition. Springer Verlag 1989, 1989.

Kor89

T. Korb. Entwurf und Implementierung einer deklarativen Sprache zur Beschreibung neuronaler Netze. Studienarbeit 789, IPVR, Universität Stuttgart, 1989.

Kub91

G. Kubiak. Vorhersage von Börsenkursen mit neuronalen Netzen. Diplomarbeit 822, IPVR, Universität Stuttgart, 1991.

KZ89

T. Korb and A. Zell. A declarative neural network description language. In Microprocessing and Microprogramming. North-Holland, August 1989.

Mac90

N. Mache. Entwurf und Realisierung eines effizienten Simulatorkerns für neuronale Netze. Studienarbeit 895, IPVR, Universität Stuttgart, 1990.

Mam92

G. Mamier. Graphische Visualisierungs--Hilfsmittel für einen Simulator neuronaler Netze. Diplomarbeit 880, IPVR, Universität Stuttgart, 1992.

MM89

P. Smolensky M. Mozer. Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems (NIPS) 1, pages 107--115, San Mateo, 1989. Morgan Kaufmann Publishers Inc.

Mol93

Martin Fodslette Moller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6:525--533, 1993.

MP69

M. Minsky and S. Papert. Perceptrons: An Introduction to Computational Geometry. The MIT Press, Cambridge, Massachusetts, 1969.

MR92

H.Braun M. Riedmiller. Rprop: A fast adaptive learning algorithm. In Proc. of the Int. Symposium on Computer and Information Science VII, 1992.

MR93

H.Braun M. Riedmiller. Rprop: A fast and robust backpropagation learning strategy. In Proc. of the ACNN, 1993.

P88

William H. Press et al. Numerical Recipes, The Art of Scientific Computing. Cambridge University Press, 1988.

Pet91

A. Petzold. Vergleich verschiedener Lernverfahren für neuronale Netze. Studienarbeit 940, IPVR, Universität Stuttgart, 1991.

RB93

M Riedmiller and H Braun. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In Proceedings of the IEEE International Conference on Neural Networks 1993 (ICNN 93), 1993.

RCE82

D.L. Reilly, L.N. Cooper, and C. Elbaum. A neural model for category learning. Biol. Cybernet., 45, 1982.

RHW86

D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the microstructure of cognition; Vol. 1: Foundations, Cambridge, Massachusetts, 1986. The MIT Press.

Rie93

M Riedmiller. Untersuchungen zu konvergenz und generalisierungsverhalten überwachter lernverfahren mit dem SNNS. In Proceedings of the SNNS 1993 workshop, 1993.

RM86

D.E. Rumelhart and J.L. McClelland. Parallel Distributed Processing, volume 1. MIT Press, 1986.

Sch78

G. Schwarz. Annals of statistics 6, chapter Estimating the dimensions of a model. 1978.

Sch91a

M. Schmalzl. Rotations- und translationsinvariante Erkennung von maschinengeschrieben Zeichen mit neuronalen Netzen. Studienarbeit 1011, IPVR, Universität Stuttgart, 1991.

Sch91b

D. Schmidt. Anwendung neuronaler Netzwerkmodelle zur Erkennung und Klassifikation exogener und endogener Komponenten hirnelektrischer Potentiale. Studienarbeit 1010, IPVR, Universität Stuttgart, 1991.

Sch94

T. Schreiner. Ausdünnungsverfahren für Neuronale Netze. Diplomarbeit 1140, IPVR, Universität Stuttgart, 1994.

Shi68

R. Shibata. Biometrika, 68, chapter An optimal selection of Regression Variables. 1968.

Sie91

J. Sienel. Kompensation von Störgeräuschen in Spracherkennungssystemen mittels neuronaler Netze. Studienarbeit 1037, IPVR, Universität Stuttgart, 1991.

SK92

J. Schürmann and U. Kreßel. Mustererkennung mit statistischen Methoden. Technical report, Daimler-Benz AG, Forschungszentrum Ulm, Institut für Informatik, 1992.

Som89

T. Sommer. Entwurf und Realisierung einer graphischen Benutzeroberfläche für einen Simulator konnektionistischer Netzwerke. Studienarbeit 746, IPVR, Universität Stuttgart, 1989.

Soy93

T. Soyez. Prognose von Zeitreihen mit partiell recurrenten Netzen und Backpropagtion. Studienarbeit 1270, IPVR, University of Stuttgart, 1993.

SUN86

SUN. Sunview user reference manual. Technical report, SUN microsystems, 1986.

Vei91

A. Veigel. Rotations-- und rotationsinvariante Erkennung handgeschriebener Zeichen mit neuronalen Netzwerken. Diplomarbeit 811, IPVR, Universität Stuttgart, 1991.

Vog92

M. Vogt. Implementierung und Anwendung von 'Generalized Radial Basis Functions' in einem Simulator neuronaler Netze. Diplomarbeit 875, IPVR, Universität Stuttgart, 1992.

Was89

Philip D. Wasserman. Neural Computing. Van Nostrand Reinhold, New York, 1989.

Was95

Philip D. Wasserman. Advanced Methods in Neural Computing. Van Nostrand Reinhold, 1995.

Weh94

C. Wehrfritz. Neuronale netze als statistische methode zur erklärung von klassifikationen. Master's thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, Lehrstuhl für Statistik 1, 1994.

Wer88

P. Werbos. Backpropagation: Past and future. In Proceedings of the IEEE International Conference on Neural Networks, pages 343--353. IEEE Press, 1988.

WHH89

A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. Lang. Phoneme Recognition Using Time Delay Neural Networks. IEEE Transactions on Accoustics, Speech and Signal Processing, 37:328--339, 1989.

YLC90

S. Solla Y. Le Cun, J. Denker. Optimal Brain Damage. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems (NIPS) 2, pages 598--605, San Mateo, 1990. Morgan Kaufmann Publishers Inc.

You89

D.A. Young. X-Window-System - Programming and Applications with Xt. Prentice Hall, 1989.

Zel94

Andreas Zell. Simulation Neuronaler Netze. Addison-Wesley, 1994. published in German.

Zim91

P. Zimmerer. Translations-- und rotationsinvariante Erkennung von Werkstücken mit neuronalen Netzwerken. Diplomarbeit 777, IPVR, Universität Stuttgart, 1991.

Zip90

D. Zipser. Subgrouping reduces compexity and speeds up learning in recurrent networks. In D.S. Touretzky, editor, Advances in Neural Information Processing systems II, pages 638--641, San Mateo, California, 1990. Morgan Kaufmann.

ZKSB89

A. Zell, T. Korb, T. Sommer, and R. Bayer. Netsim: Ein Simulator für neuronale Netze. In GWAI-89. Springer-Verlag (Informatik-Fachberichte), 1989.

ZMS90

A. Zell, N. Mache, and T. Sommer. Applications of neural networks. In Proc. Applications of Neural Networks Conf., SPIE, volume 1469, pages 535--544, Orlando Florida, 1990. Aerospace Sensing Intl. Symposium.

ZMSK91a

A. Zell, N. Mache, T. Sommer, and T. Korb. Design of the SNNS neural network simulator. In Östreichische Artificial-Intelligence-Tagung, pages 93--102, Wien, 1991. Informatik-Fachberichte 287, Springer Verlag.

ZMSK91b

A. Zell, N. Mache, T. Sommer, and T. Korb. Recent Developments of the SNNS Neural Network Simulator. In Proc. Applications of Neural Networks Conf., SPIE, volume 1469, pages 708--719, Orlando Florida, 1991. Aerospace Sensing Intl. Symposium.

ZMSK91c

A. Zell, N. Mache, T. Sommer, and T. Korb. The SNNS Neural Network Simulator. In GWAI-91, 15. Fachtagung für künstliche Intelligenz, pages 254--263. Informatik-Fachberichte 285, Springer Verlag, 1991.

ZZ91

P. Zimmerer and A. Zell. Translations-- und rotationsinvariante Erkennung von Werkstücken mit neuronalen Netzwerken. In Informatik-Fachberichte 290, pages 51--58, München, 1991. DAGM Symposium.