Abstract
Random neural networks mimic at a very deep level the biological nervous system. However, it is difficult to meet during learning the biological constraints imposed on their parameters. In the paper two possible extensions are proposed in order to remove this difficulty. Moreover, the proposed learning algorithm is tailored to the specific architecture in order to reduce the computational cost. Two architectures are considered and illustrated by simulation tests.
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Martinelli, G., Frattale Mascioli, F.M., Panella, M., Rizzi, A. (2002). Extended Random Neural Networks. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_7
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DOI: https://doi.org/10.1007/3-540-45808-5_7
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