Abstract
We describe (supervised and unsupervised) learning rules for continuous-time Artificial Neural Nets (ANNs). For feedforward ANNs, supervised learning is achieved efficiently by modifications to the well-known Error Back-Propagation learning rule. For Feedback ANNs novel learning rules are introduced for supervised learning. The essential feature of all the learning rules is that they lend themselves to analog (all-MOS) circuit realization, and thus they are suitable for implementation using standard (analog) silicon CMOS technology. Circuit implementation of sample learning rules are demonstrated.
Results from computer simulations, SPICE simulations, as well as laboratory experiements of circuits and chips substantiate the effectiveness of these rules.
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References
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© 1992 Springer-Verlag New York, Inc.
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Salam, F.M.A. (1992). Learning Algorithms for Artifical Neural Nets for Analog Circuit Implementation. In: Page, C., LePage, R. (eds) Computing Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2856-1_22
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DOI: https://doi.org/10.1007/978-1-4612-2856-1_22
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-97719-5
Online ISBN: 978-1-4612-2856-1
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