Generally, the resolution of a problem by using soft-computing support requires several attempts for setting up a proper neural network. Such attempts consist of designing and training a neural network and can be a relevant effort for the developer. This paper proposes a toolbox that automates several steps for setting up a neural network, and provides high-level abstractions allowing a developer to choose classical network topologies and configure them as desired, as well as design a neural network from a scratch. A valuable aspect of our solution is given by the modularity of the whole design that builds on object-orientation and design patterns.
CITATION STYLE
Napoli, C., & Tramontana, E. (2015). An object-oriented neural network toolbox based on design patterns. In Communications in Computer and Information Science (Vol. 538, pp. 388–399). Springer Verlag. https://doi.org/10.1007/978-3-319-24770-0_34
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