Abstract
In the previous chapters we have discussed different models of neural networks—linear, recurrent, supervised, unsupervised, self-organizing, etc. Each kind of network relies on a different theoretical or practical approach. In this chapter we investigate how those different models can be combined. We transform each single network in a module that can be freely intermixed with modules of other types. In this way we arrive at the concept of modular neural networks.
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© 1996 Springer-Verlag Berlin Heidelberg
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Rojas, R. (1996). Modular Neural Networks. In: Neural Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-61068-4_16
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DOI: https://doi.org/10.1007/978-3-642-61068-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60505-8
Online ISBN: 978-3-642-61068-4
eBook Packages: Springer Book Archive