Abstract
In recent years there has been an increasing interest in the use of artificial neural networks (ANNs) for technical applications (e.g. Rogers 1990). Particularly attractive is the application of ANNs in those domains where at present humans outperform any currently available high performance computers, e.g. in areas like auditory perception, vision, or sensory-motor control. Neural information processing is expected to have a wide applicability in areas that require a high degree of flexibility and the ability to operate in uncertain environments where information usually is partial, fuzzy, or even contradictory. The computing power of biological neural networks stems to a large extend from a highly parallel, fine-grained and distributed processing and storage of information as well as from the capability of learning.
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References
Goser, K., Hilleringmann, U., Rückert, U., Schumacher, K., VLSI Technologies for Artificial Neural Networks, IEEE Micro, Vol. 9, No. 6, pp. 28–44, 1989.
Jain, A.K., Dubes, R.C., Algorithms for clustering data, Prentice Hall, Englewood Cliffs, New Jersey, 1988.
Kohonen, T., Associative Memory: A system-theoretical approach, Springer Verlag, 1977.
Kohonen, T., Self-organization and associative memory, Springer Series in Information Sciences 8, Heidelberg 1984.
Marks, K.M., Goser, K.F., Al Concepts for VLSI Process Modelling and Monitoring, Proc. Comp. Euro. 87, IEEE Society Press, pp. 474–477, 1987.
Michalski, R., Carbonell, J.G., Mitchell, T.M., Machine Learning — An artificial intelligence approach, Springer Verlag, Berlin 1984.
Oja, E., Neural netwoks: principal components and subspaces, Int. J. of Neural Systems, vol. 1, no. 1, pp. 61–68, 1989.
Palm, G., On Associative Memory, Biol. Cybern. 36, pp. 19–31, 1980.
Palm, G., Neural Assemblies: An Alternative Approach to Artificial Intelligence, Springer-Verlag, Berlin, 1982.
Palm, G., Computing with Neural Networks, Science, Vol. 235, pp. 1227–1228, 1987.
Palm, G., Assoziatives Gedächtnis und Gehirntheorie, (in german) Spektrum der Wissenschaft, pp. 54–64, 1988.
Palm, G., Local Learning Rules and Sparse Coding in Neural Networks, in Advanced Neural Computers, R. Eckmiller (ed.), North-Holland, 1990.
Palm, G., Palm, M., Parallel Associative Networks: The PAN-System and the Bacchus-Chip, in Proc. of the 2nd Int. Conf. Microelectronics for Neural Networks, U. Ramacher, U. Rückert, J.A. Nossek (eds.), Kyrill&Method Verlag, München 1991, pp. 411–416.
Pao, Y.H., Adaptive Pattern Recognition and Neural Networks, Addison Wesley, 1989.
Reuse, B., Neurocomputing Initiatives and Perspectives of the German Ministry of research and Technology BMFT, in ENNS Newsletter, May 1992, pp. 11–13, 1992.
Rogers, S.K. (Ed.), Applications of Artificial Neural Networks, Proc. SPIE 1294, Washington 1990.
Rückert, U., Kreuzer, L, Goser, K., A VLSI Concept for an Adaptive Associative Matrix based on Neural Networks, in: Proc. Comp. Euro. 87, IEEE Society Press, pp. 31–34, 1987.
Rückert, U., An Associative Memory with Neural Architecture and its VLSI Implementation, Los Alamitos: IEEE Computer Society Press, pp. 212–218, 1991.
Salton, G., McGill, M J., Introduction to Modern Information Retrieval, McGraw-Hill, New York, 1983.
Shaw, G.L., Palm, G., Brain Theory, World Scientific, Singapure, 1988.
Surmann, H., Möller, B, Goser, K., A distributed self-organizing fuzzy rule based system, in: Proc. of the 5th Int. Conf. on Neural Networks & their Applications, NEURO NIMES 92, pp. 187–194, 1992.
Tou, J.T., Gonzales, R.C., Pattern Recognition Principles, Addison-Wesley, Reading, MA, 1974.
Ultsch, A., Siemon, H.P., Exploratory Data Analysis: Using Kohonen Networks on Transputers, Research Report No. 329, University of Dortmund, 1989.
Ultsch, A., Siemon, H.P., Kohonen’s Self Organizing Feature Maps for Exploratory Data Analysis, Proc. of the Int. Neural Network Conf., Kluwer Academic Press, Dordrecht, pp. 305–308, 1990.
Ultsch, A., Hannuschka, R., Hartmann, U., Weber, V., Learning of Control Knowledge for Symbolic Proofs with Backpropagation Networks, in Parallel Processing in Neural Systems and Computers, Eckmiller, R., Hartmann, G., Hauske, G. (eds.), North- Holland 1990.
Ultsch, A., Konnektionistische Modelle und ihre Integration mit wissensbasierten Systemen, (in german) Research Report No. 396, University of Dortmund, Dept. of Computer Science, 1991.
Ultsch, A., Höffgen, K.-U., Automatische Wissensakquisition ßr Fuzzy-Expertensysteme aus selbstorganisierenden neuronalen Netzen, Research Report No. 404 (in german), University of Dortmund, Dept. of Computer Science, 1991.
Ultsch, A., Self-Organizing Neural Networks for Knowledge Acquisition, in Proc. of the ECAI 92. 10th European Conference on Artificial Intelligence, B. Neumann (ed.),Wiley & Sons Ltd, Chichester, pp. 208–210, 1992.
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Palm, G., Ultsch, A., Goser, K., Rückert, U. (1994). Knowledge Processing in Neural Architecture. In: Delgado-Frias, J.G., Moore, W.R. (eds) VLSI for Neural Networks and Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1331-9_21
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