Abstract
The goal of this work is to present an efficient implementation of the Backpropagation (BP) algorithm to train Artificial Neural Networks with general feedforward topology. This will lead us to the “consecutive retrieval problem” that studies how to arrange efficiently sets into a sequence so that every set appears contiguously in the sequence. The BP implementation is analyzed, comparing efficiency results with another similar tool. Together with the BP implementation, the data description and manipulation features of our toolkit facilitates the development of experiments in numerous fields.
This work has been partially supported by the Spanish Government under contract TIN2006-12767 and by the Generalitat Valenciana under contract GVA06/302.
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España-Boquera, S., Zamora-Martínez, F., Castro-Bleda, M.J., Gorbe-Moya, J. (2007). Efficient BP Algorithms for General Feedforward Neural Networks. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_33
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DOI: https://doi.org/10.1007/978-3-540-73053-8_33
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