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- W1553979488 abstract "AbstractSeveral implementations of Artificial Neural Networks have been reported in scientific papers. Nevertheless, these implementations do not allow the direct use of off-line trained networks because of the much lower precision when compared with the software solutions where they are prepared or modifications in the activation function. In the present work a hardware solution called Artificial Neural Network Processor, using a FPGA, fits the requirements for a direct implementation of Feedforward Neural Networks, because of the high resolution and accurate activation function that were obtained. The resulting hardware solution is tested with data from a real system to confirm that it can correctly implement the models prepared off-line with MATLAB.KeywordsArtificial Neural NetworkActivation FunctionField Programmable Gate ArrayHardware ImplementationHyperbolic TangentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1553979488 date "2004-01-01" @default.
- W1553979488 modified "2023-09-26" @default.
- W1553979488 title "Artificial Neural Networks Processor – A Hardware Implementation Using a FPGA" @default.
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- W1553979488 doi "https://doi.org/10.1007/978-3-540-30117-2_132" @default.
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