Abstract
Artificial neural networks (ANNs) are principally attractive for their high degree of parallelism, for their associative memory properties, and for their ability to swiftly compute “near-optimal” solutions to highly constrained optimization problems. In this paper we examine the essential adaptive models that have been proposed for ANNs.
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© 1993 Springer-Verlag Berlin Heidelberg
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Ligomenides, P.A. (1993). Adaptive models in neural networks. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_146
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DOI: https://doi.org/10.1007/3-540-56798-4_146
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