Abstract
In this work we describe the design and functioning of a new neural network based on vector quantification. This network, which we call SOAN (Self Organizing with Adaptative Neigborhood) has a greater degree of learning flexibility due to the use of an interaction radius between neurones which varies spatially and temporally, and an adaptative neighbourhood function. Secondly, we have introduced mechanisms into the network with the aim of guaranteeing that all of its neurones contribute as far as possible in reducing the quantification error. Finally, we have carried out several experiments obtaining highly favourable results, which after having been contrasted with those obtained with the application of the SOM network, confirm the utility and advantages of our approach.
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© 1999 Springer-Verlag Berlin Heidelberg
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Iglesias, R., Barro, S. (1999). SOAN: Self organizing with adaptive neighborhood neural network. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098217
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DOI: https://doi.org/10.1007/BFb0098217
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Print ISBN: 978-3-540-66069-9
Online ISBN: 978-3-540-48771-5
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