Abstract
Multilayer Perceptron Nets are one of the most well known architectures for Artificial Neural Networks. The high density of interconnections among neurons however, make their VLSI-realization extremely difficult. In this paper we introduce quadratic perceptrons and show that they may lead to substantial reduction in the number of required interconnections thus improving the adequacy for integration. A Multilayer Perceptron Net with quadratic perceptrons in the first layer may be trained by using backpropagation.
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© 1991 Springer-Verlag Berlin Heidelberg
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Röckmann, D., Moraga, C. (1991). Using quadratic perceptrons to reduce interconnection density in multilayer neural networks. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035881
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DOI: https://doi.org/10.1007/BFb0035881
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