Abstract
In this chapter we introduce mathematical tools, called algebraic invariants, which allow the characterization of the periodic behaviour of some classical models of neural computation. We include different ways of updating the networks: synchronous, sequential and block-sequential, which contains as particular cases the two previous ones. We also study memory iteration where the updating consider a longer history of each site. Finally, we also use algebraic invariantes to study majority networks.
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© 1990 Kluwer Academic Publishers
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Goles, E., Martínez, S. (1990). Algebraic Invariants on Neural Networks. In: Neural and Automata Networks. Mathematics and Its Applications, vol 58. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0529-0_3
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DOI: https://doi.org/10.1007/978-94-009-0529-0_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6724-9
Online ISBN: 978-94-009-0529-0
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