Abstract
Number series tests are a popular task in intelligence tests to measure a person’s ability of numerical reasoning. The function represented by a number series can be learned by artificial neural networks. In contrast to earlier research based on feedforward networks, we apply simple recurrent networks to the task of number series prediction. We systematically vary the number of input and hidden units in the networks to determine the optimal network configuration for the task. While feedforward networks could solve only 18 of 20 test series, a very small simple recurrent network could find a solution for all series. This underlines the importance of recurrence in such systems, which further is a basic concept in human cognition.
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Glüge, S., Wendemuth, A. (2013). Solving Number Series with Simple Recurrent Networks. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_43
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DOI: https://doi.org/10.1007/978-3-642-38637-4_43
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