Abstract
Artificial Neural Networks (ANNS) base their processing capabilities in parallel architectures. This makes them useful to solve pattern recognition, system identification and control problems. In particular, it is extremely important for commercial banks to set up an early bank credit risk warning system. The authors set up early warning indicators for commercial bank credit risk, and carry out the warning for the credit risk in advance with the help of the ANNS.
A three layer ANN has been implemented, using a custom developed sfloat24 math library, on a low cost FPGA device.
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Miglionico, M.C., Parillo, F. (2012). An Application in Bank Credit Risk Management System Employing a BP Neural Network Based on sfloat24 Custom Math Library Using a Low Cost FPGA Device. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_10
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DOI: https://doi.org/10.1007/978-3-642-31724-8_10
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