Abstract
The paper proposes new neuron model with an aggregation function based on Generalized harmonic mean of the inputs. Information-maximization approach has been used for training the new neuron model. The paper focuss on illustrating the efficiency of the proposed neuron model for blind source separation. It has been shown on various generated mixtures (for blind source separation) that the new neuron model performs far superior compared to the conventional neuron model.
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Shiblee, M., Chandra, B., Kalra, P.K. (2009). New Neuron Model for Blind Source Separation. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_4
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DOI: https://doi.org/10.1007/978-3-642-03040-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
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